Service features

Data limits

The interfaces accept the following maximum amounts of audio data with a single request:

• The WebSocket interface accepts a maximum of 100 MB of audio.
• The synchronous HTTP interface accepts a maximum of 100 MB of audio.
• The asynchronous HTTP interface accepts a maximum of 1 GB of audio.

For more information about using compression to maximize the amount of data that you can send to the service, see Data limits and compression.

Advantages of the WebSocket interface

The WebSocket interface has a number of advantages over the HTTP interface. The WebSocket interface

• Provides a single-socket, full-duplex communication channel. The interface lets the client send multiple requests to the service and receive results over a single connection in an asynchronous fashion.
• Provides a much simpler and more powerful programming experience. The service sends event-driven responses to the client's messages, eliminating the need for the client to poll the server.
• Allows you to establish and use a single authenticated connection indefinitely. The HTTP interfaces require you to authenticate each call to the service.
• Reduces latency. Recognition results arrive faster because the service sends them directly to the client. The HTTP interface requires four distinct requests and connections to achieve the same results.
• Reduces network utilization. The WebSocket protocol is lightweight. It requires only a single connection to perform live-speech recognition.
• Enables audio to be streamed directly from browsers (HTML5 WebSocket clients) to the service.
• Returns results as soon as they are available when you use a next-generation model or request interim results.

Speech or word detection

Use the new parameter speech_begin_event to receive a notification event the moment speech is detected in the audio stream. This feature allows real time applications to learn when you start speaking. A common use case for this feature is implementing barge-in in automated agent systems. Barge-in consists of interrupting audio playback when the caller starts speaking. Set the value to true to make the Speech to Text service send back a speech_begin_event response, which contains the time when speech activity is first detected within the audio stream. You can use this parameter in both standard and low latency mode.

• Parameter name: speech_begin_event
• Request parameter: speech_begin_event = true/false (boolean)
• Response object: "speech_begin_event.begin", for example: {"speech_begin_event": { "begin": }}

Audio transmission and timeouts

• Audio transmission describes how you can pass audio as a continuous stream of data chunks or as a one-shot delivery that passes all of the data at one time. With the WebSocket interface, audio data is always streamed to the service over the connection. With the HTTP interfaces, you can stream the audio or send it all at once.
• Timeouts are used by the service to ensure an active flow of data during audio streaming. When you initiate a streaming session, the service enforces inactivity and session timeouts from which your application must recover gracefully. If a timeout lapses during a streaming session, the service closes the connection.

Interim results and low latency

• Interim results are intermediate hypotheses that the service returns as transcription progresses. They are available only with the WebSocket interface. The service returns final results when a transcript is complete. With the HTTP interfaces, the service always transcribes the entire audio stream before sending any results.
• Low latency, when used with certain next-generation models, directs the service to produce final results even more quickly than the models usually do. Low latency is available with the WebSocket and HTTP interfaces. Although low latency further enhances the already improved response times of the models, it might reduce transcription accuracy. When you use the next-generation models with the WebSocket interface, low latency is required to obtain interim results.

Speech activity detection

• Speech detector sensitivity adjusts the sensitivity of the service's detection of speech activity. Use the parameter to suppress word insertions from music, coughing, and other non-speech events that can adversely affect the quality of speech recognition.
• Background audio suppression suppresses background audio based on its volume to prevent it from being transcribed as speech. Use the parameter to suppress side conversations or background noise from speech recognition.

Speech audio parsing

• End of phrase silence time specifies the duration of the pause interval at which the service splits a transcript into multiple final results in response to silence. If the service detects pauses or extended silence before it reaches the end of the audio stream, its response can include multiple final results. You can increase or decrease the pause interval to affect the results that you receive.
• Split transcript at phrase end directs the services to split a transcript into multiple final results for semantic features such as sentences. The service bases its understanding of semantic features on the base language model that you use with a request. Custom language models and grammars can also influence how and where the service splits a transcript.
• Character insertion bias specifies whether a next-generation model is to favor shorter or longer strings as it develops hypotheses during speech recognition. As it develops transcription hypotheses, the service optimizes how it parses audio to balance between competing strings of different lengths. You can indicate that the service is to bias its analysis toward shorter or longer strings.

Speaker labels

• Speaker labels identify different speakers from the audio of a multi-participant exchange. The transcription labels the words and times of each speaker's contributions to a multi-participant conversation. Speakers labels are beta functionality.

Keyword spotting and word alternatives

• Keyword spotting identifies spoken phrases that match specified keyword strings with a user-defined level of confidence. Keyword spotting is especially useful when individual phrases from the audio are more important than the full transcription. For example, a customer support system might identify keywords to determine how to route user requests.
• Word alternatives request alternative words that are acoustically similar to the words of a transcript. The words that it identifies must meet a minimum confidence threshold that is specified by the user. The service identifies similar-sounding words and provides their start and end times, as well as its confidence in the possible alternatives.

Response formatting and filtering

• Smart formatting converts dates, times, numbers, currency values, phone numbers, and internet addresses into more readable, conventional forms in final transcripts. For US English, you can also provide keyword phrases to include certain punctuation symbols in final transcripts. Smart formatting is beta functionality.
• Numeric redaction redacts, or masks, numeric data from a final transcript. Redaction is intended to remove sensitive personal information, such as credit card numbers, from final transcripts. Numeric redaction is beta functionality.
• Profanity filtering censors profanity from transcripts and metadata.

Response metadata

• Maximum alternatives provide possible alternative transcripts. The service indicates final results in which it has the greatest confidence.
• Word confidence returns confidence levels for each word of a transcript.
• Word timestamps return timestamps for the start and end of each word of a transcript.

Processing and audio metrics

• Processing metrics provide detailed timing information about the service's analysis of the input audio. The service returns the metrics at specified intervals and with transcription events, such as interim and final results. You can use the metrics to gauge the service's progress in transcribing the audio. You can request processing metrics with the WebSocket and asynchronous HTTP interfaces.
• Audio metrics provide detailed information about the signal characteristics of the input audio. The results provide aggregated metrics for the entire input audio at the conclusion of speech processing. You can use the metrics to determine the characteristics and quality of the audio. You can request audio metrics with any of the service's interfaces.