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Productivity solutions for every purpose. Dragon Professional Anywhere. Dragon Legal Anywhere. In some languages, this stub is defined in the generated client files: in Python it is named RecognizerStub , in Go it is RecognizerClient , and in Java it is RecognizerStub. After setting recognition parameters, the app sends the RecognitionRequest stream, including recognition parameters and the audio to process, to the channel and stub.
In this Python example, this is achieved with a two-part yield structure that first sends recognition parameters then sends the audio for recognition in chunks. Normally your app will send streaming audio to Krypton for processing but, for simplicity, this application simulates streaming audio by breaking up an audio file into chunks and feeding it to Krypton a bit at a time. Finally the app returns the results received from the Krypton engine. The results may be long or short depending on the length of your audio, the recognition parameters, and the fields included by the app.
See Results. The audio file says:. It's Monday morning and the sun is shining. I'm getting ready to walk to the train and commute into work. I'll catch the seven fifty-eight train from Cedar Park station. It will take me an hour to get into town. This example transcribes the audio file weather The file says:. There is more snow coming to the Montreal area in the next few days. We're expecting ten centimeters overnight and the winds are blowing hard. Our radar and satellite pictures show that we're on the western edge of the storm system as it continues to track further to the east.
In both these examples, Krypton performs the recognition using only the data pack. For these simple sentences, the recognition is nearly perfect. Once you have experimented with basic recognition, you can add resources such as domain language models and wordsets to improve recognition of specific terms and language in your environment.
For example, you might add resources containing names and places in your business. See Prerequisites from Mix and the example at the right. You could instead read the wordset from a local file, as shown in Inline wordsets , or as a compiled wordset in Compiled wordsets.
The audio file in this example, abington. The recording says:. I'm going on a trip to Abington Piggots in Cambridgeshire, England. I'm speaking to you from the town of Cogenhoe [cook-no] in Northamptonshire. We spent a week in the town of Llangollen [lan-goth-lin] in Wales. Have you ever thought of moving to La Jolla [la-hoya] in California. But when all the place names are defined, either in the DLM or in a wordset such as the following, there is perfect recognition.
A simple Python 3. To run it:. Copy the script into a file named run-python-client. Place both files in the directory above the proto and Python stub files. This example uses a DLM and an inline wordset. To request recognition without a DLM or wordset, comment out the resources line:. This sample Python app accepts an audio file and transcribes it. Run it from the shell script, which generates a token and runs the app. Pass it the name of an audio file. The run-python-client.
You may instead incorporate the token-generation code within the application, reading the credentials from a configuration file. To use a compiled wordset created with the Training API see Sample Python app: Training , change the resources line to reference it instead of the inline wordset:. This application prints just a few selected fields. For examples of adding extra individual fields, see Dsp , Hypothesis , and DataPack.
To display all possible information returned by Krypton, replace these lines:. For an example of these longer—potentially much longer—results, see Fields chosen by app. See Training API for the details of the methods. A sample Python application lets you try out the training API. Download this zip file, sample-python-training-app.
The file contains:. Proto files and generated client stubs for training and RPC messaging. See gRPC setup. Credentials from Mix a client ID and secret.
See Prerequisites from Mix. A source JSON wordset as text or in a file. See Wordsets for information on source wordsets. You can use the application to compile wordsets, get information about existing compiled wordsets, and delete compiled wordsets.
Once you have created the compiled wordsets, you can use them in the recognizer API. See ResourceReference. For a quick check that the application is working, and to see the arguments it accepts, run the client app directly using the help -h or --help option. By default the file is named flow. By default this is localhost but the sample run script specifies the Mix service, asr. Before running the application against the Krypton server, edit the sample files for your environment: the script file that runs the app and the input files.
The sample run script, run-training-client. Alternatively, you may generate the token using the application itself, by providing your credentials in the oauthURL, clientID, clientSecret, and oauthScope arguments. The client application must provide an access token to be able to access the Training service. The client application uses the client ID and secret from the Mix Dashboard see Prerequisites from Mix to generate an access token from the Nuance authorization server.
When calling the Training service, the scope in the authorization request is asr. You may also include the Recognizer scope, asr , if you are qualified for both services. Also add your information to the input files. Most files contain the Mix-specific location of the domain LM that contains the entity or entities your wordset extends, a URN for the compiled wordset, and a wordset in compressed JSON.
You may create a new context tag for the wordset or use the same tag as its companion DLM. You may optionally leave this wordset as is and provide your own source wordset in a file containing either expanded or compressed JSON. The sample package includes a wordset file that you may edit: see places-wordset. Optionally add a line at the top of the files to identify your Python environment, for example:!
To compile a wordset, you send the training request and watch as the job progresses. The results are streamed back from the server as the compilation proceeds, so you can see the progress of the job. In this example, the wordset being created is named places-compiled-ws. Open the run script, run-training.
You must provide the source wordset either in the flow file or as in this example using the --wsFile option in the run script. See the results at the right. The training API reads the wordset from the file, then compiles it as places-compiled-ws and stores it in the Mix environment. You can then reference it your recognition requests see ResourceReference using the URN you provided, for example:.
It returns metadata information but not the source JSON wordset. In this example, the wordset being referenced is places-compiled-ws. It removes the wordset permanently from the Mix environment. In this example, the wordset being deleted is places-compiled-ws. Existing wordset: If you use the same wordset name in a compile request, you receive an error that the wordset already exists.
You can either use a new name or delete the existing wordset before creating it again. A single Recognizer service provides a single Recognize method supporting bi-directional streaming of requests and responses.
The client first provides a recognition request message with parameters indicating at minimum what language to use. Optionally, it can also include resources to customize the data packs used for recognition, and arbitrary client data to be injected into call recording for reference in offline tuning workflows.
In response to the recognition request message, Krypton returns a status message confirming the outcome of the request. Status messages include HTTP-aligned status codes. A failure to begin recognizing is reflected in a 4xx or 5xx status as appropriate. Cookies returned from resource fetches, if any, are returned in the first response only.
Termination conditions include:. If the client cancels the RPC, no further messages are received from the server. If the server encounters an error, it attempts to send a final error status and then cancels the RPC. The results returned by Krypton applications can range from a simple transcript of an individual sentence to thousands of lines of JSON information.
The scale of these results depends on two main factors: the recognition parameters in the request and the fields chosen by the the client application. In these examples, the application displays only a few basic fields. If the application displays more fields, the results include all those additional fields. See Fields chosen by app next. The result type specifies the level of detail that Krypton returns in its streaming result.
This parameter has three possible values:. To show this information to users, the app can determine the result type and display it using code such as this: elif message. Partial results of each sentence are delivered as soon as speech is detected, but with low recognition confidence. These results usually change as more speech is processed and the context is better understood. Final results are returned at the end of each sentence. Partial results are delivered after a slight delay to ensure that the recognized words do not change with the rest of the received speech.
Some data packs perform additional processing after the initial recognition. The transcript may change slightly during this second pass, even for immutable partial results. For example, Krypton originally recognized "the seven fifty eight train" as "the A-Train" but adjusted it during a second pass, returning "the train" in the final version of the sentence.
The combination of these two parameters returns different results. In all cases, the actual returned fields also depend on which fields the client application chooses to display. The utterance detection modes do not support all the timeout parameters in RecognitionParameters. See Timeouts and detection modes. Another way to customize your results is by selecting specific fields, or all fields, in your application. From the complete results returned by Krypton, the application selects the information to display to users.
It can be just a few basic fields or the complete results in JSON format. In this example, the application displays only a few essential fields: the status code and message, plus the result type and the formatted text of the best hypothesis of each sentence. See RecognitionResponse - Result for all fields.
Typically several hypotheses are returned for each sentence, showing confidence levels of the hypothesis as well as formatted and minimally formatted text of the sentence. See Formatted text for the difference between formatted and minimally formatted text. In this example, the result type is FINAL, meaning Krypton returns several hypotheses for each sentence but only the final version of each hypothesis.
With result type PARTIAL, the results can be much longer, with many variations in each hypothesis as the words in the sentence are recognized and transcribed. Formatted text includes initial capitals for recognized names and places, numbers expressed as digits, currency symbols, and common abbreviations. In minimally formatted text, words are spelled out but basic capitalization and punctuation are included. See Formatting. Each scheme is a collection of many options see Formatting options below , but the defining option is PatternBias, which sets the preferred pattern for numbers that cannot otherwise be interpreted.
The values of PatternBias give their name to most of the schemes: date, time, phone, address, and default. The PatternBias option cannot be modified, but you may adjust other options using formatting options. Formatting schemes help Krypton interpret ambiguous numbers, e.
The formatting schemes date, time, phone, and address tell Krypton to prefer one pattern for ambiguous numbers. By setting the formatting scheme to date, time, phone, or address, you instruct Krypton to interpret these ambiguous numbers as the specified pattern.
For example, if you know that the utterances coming into your application are likely to contain dates rather than times, set scheme to date. For example, Krypton identifies this as an address: "My address is seven twenty six brookline avenue cambridge mass oh two one three nine. Oh two one three nine". With all other schemes, the text is formatted as a standard address: "My address is Brookline Ave.
This scheme is the default. It has the same effect as not specifying a scheme. If Krypton cannot determine the format of the number, it interprets it as a cardinal number. The default scheme formats numbers as numerals from 10 upwards: one, two, three All options are part of the current formatting scheme default if not specified but can be set on their own to override the current setting.
The available options depend on the data pack. See Formatting options by language. Japanese data packs support the formatting options shown at the right. In these data packs, two options work together to specify how numbers are displayed. For words containing numbers, the formatting output depends on whether the word is defined in the system. If the word containing a number is not defined in the system, the formatting output depends on the context and the formatting scheme in effect date, time, price, address, and so on.
What's the difference? These schemes tell Krypton how to interpret ambiguous numbers , while the options tell Krypton how to format text for display. For example:. This is the default setting. When you set formatting options, be aware of the default for the scheme to which it belongs.
Each language supports a different set of formatting options, which you may modify to customize the way that Krypton formats its results. See Formatting options. Krypton offers three timers for limiting user silence and recognition time: a no-input timer, a recognition timer, and an end-of-utterance timer.
By default, the no-input timer starts when recognition starts, but has an infinite timeout, meaning Krypton simply waits for the user to speak and never times out.
If a prompt plays as recognition starts, the recognition may time out before the user hears the prompt. The timeout parameters are not supported in all utterance detection modes. See Timeouts and detection modes next. In RecognitionParameters, the utterance detection modes do not support all the timeout parameters.
A wakeup word is a word or phrase that users can say to activate an application, for example "Hey Nuance" or "Hi Dragon. Each wakeup word consists of one or more space-separated literals with no markup or control characters. For best recognition results, include several variations of the wakeup word your application can accept. See Only a wakeup word below for an example. Specifically, in result - Hypothesis :. If the wakeup word is the only input, it is not filtered. In all partial results, wakeup words are reported normally.
They are not removed from partial or immutable partial results. If the user does not say any of the wakeup words, or if Krypton does not recognize them, the transcription proceeds without error, reporting all words spoken by the user. Notice the wakeup word is not filtered from the results. In the context of Krypton, resources are objects that facilitate or improve recognition of user speech.
Resources include data packs, domain language models, wordsets, builtins, and speaker profiles. Krypton works with one or more factory data packs, available in several languages and locales. The data pack includes these neural network-based components:. The base acoustic model is trained to give good performance in many acoustic environments.
The base language model is developed to remain current with popular vocabulary and language use. As such, Krypton paired with a data pack is ready for use out-of-the-box for many applications.
Each recognition turn leverages a weighted mix of builtins, domain LMs, and wordsets. See Resource weights. The available builtins depends on the data pack.
For American English data packs, for example, the builtins are:. To use a builtin in Krypton, declare it with builtin in RecognitionResource. Domain LMs Each data pack supplied with Krypton provides a base language model that lets Krypton recognize the most common terms and constructs in the language and locale.
You may complement this language model with one or more domain-specific models, called domain language models domain LMs or DLMs. Each DLM is based on sentences from a specific environment, or domain, and may include one or more entities , or collections of terms used in that environment. Krypton accepts up to ten DLMs, which are weighted along with other recognition objects.
There is no fixed limit for the number of inline wordsets, but for performance reasons a maximum of 10 is recommended. The topic is known as a use case when creating a project in Mix. Wordsets are declared with RecognitionInitMessage - RecognitionResource , either as an inline wordset or a compiled wordset. The source wordset is defined in JSON format as a one or more arrays. Each array is named after an entity defined within a DLM to which words can be added at runtime.
Entities are templates that tell Krypton how and where words are used in a conversation. The wordset adds to the existing terms in the entity, but applies only to the current recognition session. The terms in the wordset are not added permanently to the entity. The wordset includes additional values for one or more entities. The wordset may contain terms for multiple entities. When not supplied, Krypton guesses the pronunciation of the word from the literal.
Include a spoken form only if the literal is difficult to pronounce or has an unusual pronunciation in the language.
When a spoken form is supplied, it is the only source for recognition: the literal is not considered. If the literal pronunciation is also valid, you should include it as a spoken form.
For example, the city of Worcester, Massachusetts is pronounced wuster , but users reading it on a map may say it literally, as worcester. To allow Krypton to recognize both forms, specify:. Other special characters and punctuation may affect recognition and should be avoided where possible in both the literal and spoken fields. The literal field may contain special characters such as! In this case, also include a spoken form without special characters, for example:.
Krypton includes the special characters in the return value, for example, when the user says "I'd like to order an extra moz pizza":. See Before and after DLM and wordset to see the difference that a wordset can make on recognition. Krypton supports both source and compiled wordsets.
You can either provide the source wordset in the request or reference a compiled wordset using its URN in the Mix environment. You may provide a source wordset directly in the request or read it from a local file using a programming language function.
Notice that a spoken form is provided only for terms that do not follow the standard pronunciation rules for the language. You may instead store the source wordset in a local JSON file and read the file places-wordset. Alternatively, you may reference a compiled wordset that was created with the training API. You may use either inline or compiled wordsets to aid in recognition. The size of your wordset often dictates the best form:.
Small wordsets, containing or fewer terms, are suitable for inline use. You can include these with each recognition request at runtime. The wordset is compiled behind the scenes and applied as a resource. Larger wordsets can be compiled ahead of time using the training API.
The compiled wordset is stored in Mix and can then be referenced and loaded as an external resource runtime. This strategy improves latency significantly for large wordsets. Application-level wordset. User-level wordset. Once the wordset is compiled, it is stored on Mix and can be referenced at runtime by a client application using the same DLM and wordset URNs.
When creating a compiled wordset, the request message has a maximum size of 4 MB. A gRPC error is generated if you exceed this limit. The wordset must be compatible with the companion DLM, meaning it must have the same locale and reference entities in the DLM. The context tag used for the wordset does not have to match the context tag of the companion DLM but it may provide easier wordset management to use the same context tag for both DLM and its associated wordsets.
If your application uses both services and requires large wordsets for both, you must compile them separately for each service. Wordsets are available for 28 days after compilation, after which they are automatically deleted and must be compiled again.
Existing compiled wordsets can be updated. Compiling a wordset using an existing wordset URN replaces the existing wordset with the newer version if:.
Wordsets can also be manually deleted if no longer needed. Once deleted, a wordset is completely removed and cannot be restored. Speaker adaptation is a technique that adapts the acoustic model and improves speech recognition based on qualities of the speaker and channel. The best results are achieved by updating the data pack's acoustic model in real time based on the immediate utterance. The user id must be a unique identifier for a speaker, for example:. The first time you send a request with a speaker profile, Krypton creates a profile based on the user id and stores the data in the profile.
On subsequent requests with the same user id, Krypton adds the data to the profile, which adapts the acoustic model for that specific speaker, providing custom recognition. After the Krypton session, the adapted data is saved by default.
The overall time-to-live TTL for speaker profiles is 14 days, meaning they are saved for 14 days and then discarded. A wordset, two DLMs, and one builtin are declared in this example, leaving the base LM with a weight of 0. In each recognition turn, Krypton uses a weighted mix of resources: the base LM plus any builtins, DLMs, and wordsets declared in the recognition request.
You may set specific weights for DLMs and builtins. You cannot set a weight for wordsets. By default, the base language model has a weight of 1. If other resources exceed 0. In this case, the words in the base LM are still recognized, but with lower probability than words in the DLMs and other resources. The default weight of each declared builtin is 0.
The default weight of each declared DLM is 0. The weight of each wordset is tied to the weight of its DLM. Wordsets also have a small fixed weight 0. This weight applies to all wordsets together. If you wish to emphasize one or more DLMs at the expense of the base LM, give them a combined weight of 1. In the example at the right, the base LM has little effect on recognition. The proto files provide the following default values for messages in the RecognitionRequest sent to Krypton.
Mandatory fields are shown in bold. The values shown here are the values set in the sample configuration files default. Krypton provides protocol buffer. These files contain the building blocks of your speech recognition applications.
See Client app development and Sample Python app for scenarios and examples in Python. The proto files define a Recognizer service with a Recognize method that streams a RecognitionRequest and RecognitionResponse. Details about each component are referenced by name within the proto file. The Recognizer service offers one RPC method to perform streaming recognition. The method consists of a bidirectional streaming request and response message.
Input stream messages that request recognition, sent one at a time in a specific order. The first mandatory field sends recognition parameters and resources, the final field sends audio to be recognized. Included in Recognize method. Krypton is a real-time service and audio should be streamed at a speed as close to real time as possible. For the best recognition results, we recommend an audio chunk size of 20 to milliseconds.
Input message that initiates a new recognition turn. Included in RecognitionRequest. Input message that defines parameters for the recognition process. Included in RecognitionInitMessage. All others are optional.
See Defaults for a list of default values. Mandatory input message containing the audio format of the audio to transcribe. Included in RecognitionParameters. Input message defining A-law audio format. Included in AudioFormat. Input message defining Opus packet stream decoding parameters. Input message defining Ogg-encapsulated Opus audio stream parameters. The recommended encoder settings for Opus for speech recognition are:. Please note that Opus is a lossy codec, so you should not expect recognition results to be identical to those obtained with PCM audio.
Input field specifying how sentences utterances should be detected and transcribed within the audio stream. The detection modes do not support all the timer parameters in RecognitionParameters.
Input and output field specifying how results for each sentence are returned. See Results for examples. As output in Result , it indicates the actual result type that was returned:.
Input message containing boolean recognition parameters. The default is false in all cases. By default, this timer starts when recognition begins. See Timers. By default, data is stored. By default, call logs, audio, and metadata are collected. They are still reflected in logs.
This option does not affect words that are capitalized by definition, such as proper names, place names, etc. See example at right.
Even when true, words from the base LM are still recognized, but with lower probability. This field is ignored in some situations. See Wakeup words. Input message specifying how the results are presented, using keywords for formatting types and options supported by the data pack. See Formatted text. Input message that starts the recognition no-input timer. This setting is only effective if timers were disabled in the recognition request.
Input message the client sends when starting the no-input timer. Included in ControlMessage. RecognitionResource RecognitionResource example. Input message for fetching an external DLM or settings file that exists in your Mix project, or for creating or updating a speaker profile. Included in RecognitionResource. See Domain LMs and Speaker profiles.
The format of the URN reference depends on the resource. In these examples, the context tag is names-places and the language code is eng-USA. Speaker profiles do not use URNs. One or more words or phrases that activate the application. Input field defining the content type of an external recognition resource. Included in ResourceReference.
See Resources. Input field setting the weight of the domain LM or builtin relative to the data pack, as a keyword. Wordsets and speaker profiles do not have a weight. Input field specifying whether the domain LM or wordset will be used for one or many recognition turns.
Output stream of messages in response to a recognize request. The response contains all possible fields of information about the recognized audio, and your application may choose to print all or some fields.
The sample application prints only the status and the best hypothesis sentence, and other examples also include the data pack version and some DSP information. Your application may instead print all fields to the user with in Python a simple print message.
In this scenario, the results contain the status, start-of-speech information, followed by the result itself, consisting overall information then several hypotheses of the sentence and its words, including confidence scores.
Output message indicating the status of the job. Included in RecognitionResponse. See Status codes for details about the codes. The message and details are developer-facing error messages in English. User-facing messages should be localized by the client based on the status code.
Output message containing the start-of-speech message. Output message containing the result, including the result type, the start and end times, metadata about the job, and one or more recognition hypotheses.
See Results and Formatted text for examples of results in different formats. For other examples, see Dsp , Hypothesis , and DataPack. Output message containing information about the recognized sentence in the result. Included in Result. Output message containing digital signal processing results.
Included in UtteranceInfo. Output message containing one or more proposed transcripts of the audio stream. Each variation has its own confidence level along with the text in two levels of formatting. The recognizer determines rejection based on an internal algorithm.
If the audio input cannot be assigned to a sequence of tokens with sufficiently high probability, it is rejected. Output message containing one or more recognized words in the hypothesis, including the text, confidence score, and timing information.
Included in Hypothesis. Output message containing information about the current data pack. Included in Notification. Krypton provides a set of protocol buffer. These files allow you to compile and manage large wordsets for use with your Krypton applications:. See RPC status messages. Once you have transformed the proto files into functions and classes in your programming language using gRPC tools see gRPC setup , you can call these functions from your application to request compile and manage wordsets.
See Sample Python app: Training for scenarios in Python. You may use these proto files in conjunction with the other Krypton proto files described in Recognizer API.
The proto file defines a Training service with several RPC methods for creating and managing compiled wordsets. This shows the structure of the messages and fields for each method. Job status refers to the condition of the job that is compiling the wordset. Request status refers to the condition of the gRPC request. Its values are set in nuance. The Training service offers five RPC methods to compile and manage wordsets.
Each method consists of a request and a response message. For examples of using all these methods, see Sample Python app: Training.
|Free nuance pdf editor||Built for healthcare. Retry request as is later. Nuance Mix—Creativity unleashed. The URL to offer to the client, containing help information. The format of the URN reference depends on the message and resource. When not supplied, Krypton guesses the pronunciation of the word from the literal.|
|Nuance voice recognition api||Reconition may optionally leave this wordset as is and provide your own source wordset in a file containing either expanded or compressed JSON. Tell us here. Each DLM is based nuance voice recognition api sentences from a specific environment, or domain, and may include one or more entitiesor collections of terms used in that environment. This scheme is the default. Please note that the Training API is available only in specific geographies. This parameter has three possible values: SINGLE default : Return recognition results for one sentence utterance only, ignoring any trailing audio. See example at right.|
|Highmark bcbs il||The first time you send a request with a speaker profile, Krypton creates a profile based on the user id and stores the data in the profile. The message and details are developer-facing error messages in English. Slightly click text of the result, e. All options are part of the current formatting scheme default if not specified but can be set on their own to override the current setting. Established across the globe. The format of the URN reference depends on the message and resource.|
|Ordering refills online emblemhealth||It has the same effect as not specifying a scheme. Wordsets do not take recognitiln weight. Mandatory fields are shown in bold. Uses variable-length encoding. The detection modes do not support all the timer parameters in RecognitionParameters. For arbitrary limitations e.|
|Nuance voice recognition api||149|
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