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Azure Custom Speech Service

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Enhancing Speech Recognition Accuracy with Azure Custom Speech Service

Create a Test

To test the accuracy of your custom model, you can create a test in the Azure AI Foundry portal. This test requires a collection of audio files and corresponding transcriptions. By comparing your custom model's accuracy with a base model or another custom model, you can evaluate the word error rate (WER) to measure speech recognition results.

Get Test Results

Once you have created a test, you should get the test results to analyze the accuracy of the models. By evaluating the word error rate (WER) compared to speech recognition results, you can determine the effectiveness of the models in transcribing audio data.

Evaluate Word Error Rate (WER)

The industry standard for measuring model accuracy in speech recognition is word error rate (WER). WER calculates the number of incorrectly identified words divided by the total number of words in the human-labeled transcript. It accounts for insertion, deletion, and substitution errors to provide a percentage value representing the accuracy of the model.

Resolve Errors and Improve WER

To enhance the accuracy of your speech recognition model, analyzing and addressing the errors identified in the WER calculation is crucial. A lower WER percentage indicates better model quality. By understanding the distribution of errors and their causes, such as weak audio signal strength or insufficient domain-specific terms, you can make targeted improvements to enhance model performance.

Evaluate Token Error Rate (TER)

In addition to WER, Token Error Rate (TER) offers an extended measurement of model accuracy by evaluating the recognition of tokens in the human-labeled transcript. TER considers factors like punctuation and capitalization to assess the quality of the end-to-end display format. By calculating TER based on token level errors, you can gain more insight into the model's performance beyond word-level accuracy.

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Empower Your Applications with Azure Custom Speech Service

Introduction to Azure Custom Speech Service

Azure Custom Speech Service is a customizable speech-to-text service that allows developers to tailor speech recognition models to better suit their specific needs. By providing the ability to fine-tune models with domain-specific data, businesses can achieve higher accuracy in transcribing audio content. This service offers a range of features and tools to enhance speech recognition capabilities.

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Azure Custom Speech Service: Language and Voice Support

Overview of Language Support

The Azure Custom Speech Service offers a wide range of language and voice support for various functionalities, including speech to text, text to speech, pronunciation assessment, speech translation, language identification, speaker recognition, custom keyword detection, and intent recognition. This support enables users to interact with the service in their preferred language, enhancing accessibility and usability.

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Empower Your Solutions with Azure Custom Speech Service

Recent highlights

Azure Custom Speech Service offers fast transcription, making it possible to transcribe audio at a quicker pace than the actual audio duration. Moreover, users can now benefit from the Azure AI Speech Toolkit extension available for Visual Studio Code users. This extension includes a variety of speech quick-starts and scenario samples that can be easily executed with a few clicks. Additionally, Azure Custom Speech Service introduces high definition (HD) voices in public preview for enhanced understanding, emotion detection in input text, and real-time tone adjustment. Another exciting addition is the availability of video translation in the Azure AI Speech service, offering users the capability to translate videos effectively. The service also now supports OpenAI text to speech voices for a diverse range of voice options. Users can leverage the custom voice API to create and manage personalized neural voice models efficiently.

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Enhancing Speech Recognition Accuracy with Azure Custom Speech Service

How Custom Speech Works

Azure Custom Speech Service allows users to enhance the accuracy of speech recognition for their applications by creating custom speech models. These custom models can be utilized for real-time speech to text, speech translation, and batch transcription. The service initially leverages a Universal Language Model as a base model, trained with Microsoft-owned data, covering common spoken language. However, users can improve recognition by training a custom model with domain-specific vocabulary and audio data to suit application requirements.

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Advancing Speech Recognition with Azure Custom Speech Service

Overview of Language Support

The Azure Custom Speech Service offers comprehensive language support for various functionalities such as speech to text, text to speech, pronunciation assessment, speech translation, speaker recognition, and more. Users can leverage this service to work with different languages and dialects, enhancing accessibility and usability across diverse linguistic landscapes.

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