Jinshan contributed to the Azure-Samples/cognitive-services-speech-sdk repository by engineering features that advanced speech recognition, pronunciation assessment, and real-time avatar interactions. Leveraging C#, Java, and Swift, Jinshan refactored scoring algorithms to support multilingual and unscripted speech, integrated Azure OpenAI for deeper content feedback, and improved token alignment for zh-CN pronunciation accuracy. Jinshan also developed REST API-based pronunciation assessment and enhanced sample code organization for language-learning scenarios. The work included backend and iOS development, WebRTC integration, and robust SDK maintenance, resulting in a more maintainable, extensible codebase that improved reliability, language coverage, and developer onboarding for speech-driven applications.
October 2025 monthly work summary for Azure-Samples/cognitive-services-speech-sdk, focusing on zh-CN pronunciation assessment accuracy through token alignment improvements. Delivered a feature to align recognition output with reference text for zh-CN, enabling more reliable pronunciation scoring and better developer experience.
October 2025 monthly work summary for Azure-Samples/cognitive-services-speech-sdk, focusing on zh-CN pronunciation assessment accuracy through token alignment improvements. Delivered a feature to align recognition output with reference text for zh-CN, enabling more reliable pronunciation scoring and better developer experience.
September 2025: Monthly summary for Azure-Samples/cognitive-services-speech-sdk. Focused on enhancing speech recognition scoring for multilingual and unscripted speech, delivering a robust scoring refactor and new evaluation parameters to improve accuracy, flexibility, and language coverage.
September 2025: Monthly summary for Azure-Samples/cognitive-services-speech-sdk. Focused on enhancing speech recognition scoring for multilingual and unscripted speech, delivering a robust scoring refactor and new evaluation parameters to improve accuracy, flexibility, and language coverage.
In August 2025, progress focused on structuring language-learning pronunciation scenarios and expanding multi-language capabilities in the Azure-Samples/cognitive-services-speech-sdk. Key activities included reorganizing and consolidating pronunciation assessment samples by language and platform into dedicated scenario folders to improve organization, discoverability, and maintainability, with corresponding .NET Core variants. We also implemented REST API-based pronunciation assessment integration and refined score calculations to support continuous pronunciation across multiple languages. Additionally, alignment enhancements and mispronunciation tagging provide richer, actionable feedback for learners. These efforts collectively raise product quality, accelerate contributor onboarding, and enable faster iteration on language-learning scenarios across the SDK.
In August 2025, progress focused on structuring language-learning pronunciation scenarios and expanding multi-language capabilities in the Azure-Samples/cognitive-services-speech-sdk. Key activities included reorganizing and consolidating pronunciation assessment samples by language and platform into dedicated scenario folders to improve organization, discoverability, and maintainability, with corresponding .NET Core variants. We also implemented REST API-based pronunciation assessment integration and refined score calculations to support continuous pronunciation across multiple languages. Additionally, alignment enhancements and mispronunciation tagging provide richer, actionable feedback for learners. These efforts collectively raise product quality, accelerate contributor onboarding, and enable faster iteration on language-learning scenarios across the SDK.
April 2025 (Azure-Samples/cognitive-services-speech-sdk) delivered key feature cleanups and stability improvements that reduce maintenance, improve reliability, and enhance user experience in avatar interactions. The work focused on removing the content score calculation across core and sample projects and stabilizing Talking Avatar behavior through API support and robust reconnection handling.
April 2025 (Azure-Samples/cognitive-services-speech-sdk) delivered key feature cleanups and stability improvements that reduce maintenance, improve reliability, and enhance user experience in avatar interactions. The work focused on removing the content score calculation across core and sample projects and stabilizing Talking Avatar behavior through API support and robust reconnection handling.
March 2025 monthly summary for Azure-Samples/cognitive-services-speech-sdk. Delivered features and reliability improvements that enhance feedback quality, recognition robustness, and user experience, translating into clearer guidance for developers and more stable sample apps. Key outcomes include: - Pronunciation Assessment content scoring enhanced by integrating Azure OpenAI, enabling scoring based on language aspects for deeper, more actionable feedback. Commits: 93eb392e26fdda2848998422053edf8c9fa34f82; 92f377e8e273f576e1ca7147c31f436e14f0b53d - Speech endpoint configuration and WebSocket reliability improved for private endpoints, ensuring correct URI handling and stable connections for speech synthesis. Commit: 8806a7b43b7ab095f8ba61e73807295402ed91b1 - Configurable speech recognition silence timeout and lowercase normalization to improve segmentation accuracy and robustness across scenarios. Commits: b954f66302a75539226eb8511ff9a6658e814b2c; 176f24210eeb5bb871f8262693d845047b9bbf1b - TTS Avatar reconnection stability and UX enhancements, enabling continued speaking after reconnection and refining auto-reconnect logic to avoid loops. Commits: 76e1b649279b26b770cb910e2acc7a6ce0cebeb9; 4add8e1e0d59de5f4c9b6f3881fda9ed21203a06
March 2025 monthly summary for Azure-Samples/cognitive-services-speech-sdk. Delivered features and reliability improvements that enhance feedback quality, recognition robustness, and user experience, translating into clearer guidance for developers and more stable sample apps. Key outcomes include: - Pronunciation Assessment content scoring enhanced by integrating Azure OpenAI, enabling scoring based on language aspects for deeper, more actionable feedback. Commits: 93eb392e26fdda2848998422053edf8c9fa34f82; 92f377e8e273f576e1ca7147c31f436e14f0b53d - Speech endpoint configuration and WebSocket reliability improved for private endpoints, ensuring correct URI handling and stable connections for speech synthesis. Commit: 8806a7b43b7ab095f8ba61e73807295402ed91b1 - Configurable speech recognition silence timeout and lowercase normalization to improve segmentation accuracy and robustness across scenarios. Commits: b954f66302a75539226eb8511ff9a6658e814b2c; 176f24210eeb5bb871f8262693d845047b9bbf1b - TTS Avatar reconnection stability and UX enhancements, enabling continued speaking after reconnection and refining auto-reconnect logic to avoid loops. Commits: 76e1b649279b26b770cb910e2acc7a6ce0cebeb9; 4add8e1e0d59de5f4c9b6f3881fda9ed21203a06
January 2025: Delivered a feature refinement in Azure-Samples/cognitive-services-speech-sdk by introducing a LinkedList-based approach to store start and end offsets in SpeechRecognitionSamples.java, enabling efficient access to the earliest offsets via getFirst(). This was accompanied by a minor refactor to improve data structure usage in the sample code. No critical bugs were reported or fixed this month. Overall, the change improves sample reliability and readability, lays groundwork for future performance optimizations, and supports easier maintainability for developers integrating the speech SDK in samples.
January 2025: Delivered a feature refinement in Azure-Samples/cognitive-services-speech-sdk by introducing a LinkedList-based approach to store start and end offsets in SpeechRecognitionSamples.java, enabling efficient access to the earliest offsets via getFirst(). This was accompanied by a minor refactor to improve data structure usage in the sample code. No critical bugs were reported or fixed this month. Overall, the change improves sample reliability and readability, lays groundwork for future performance optimizations, and supports easier maintainability for developers integrating the speech SDK in samples.
November 2024: Real-time talking avatar generation on iOS delivered as part of the Azure-Samples/cognitive-services-speech-sdk. The feature demonstrates end-to-end integration of a Swift-based iOS app with Microsoft Cognitive Services Speech SDK and WebRTC to render an avatar that speaks input text with synchronized lip movements. The sample app supports start/stop sessions, text input, and live avatar playback, illustrating a practical pathway for immersive AI-driven interactions.
November 2024: Real-time talking avatar generation on iOS delivered as part of the Azure-Samples/cognitive-services-speech-sdk. The feature demonstrates end-to-end integration of a Swift-based iOS app with Microsoft Cognitive Services Speech SDK and WebRTC to render an avatar that speaks input text with synchronized lip movements. The sample app supports start/stop sessions, text input, and live avatar playback, illustrating a practical pathway for immersive AI-driven interactions.
Concise monthly summary for 2024-10 focusing on feature delivery in the Speech Recognition SDK for Azure-Samples/cognitive-services-speech-sdk, including session ID logging and enhanced scoring metrics, with updates to sample code and support for Chinese inputs. No major bugs fixed this month. Business value: improved speech quality evaluation, telemetry, and localization support.
Concise monthly summary for 2024-10 focusing on feature delivery in the Speech Recognition SDK for Azure-Samples/cognitive-services-speech-sdk, including session ID logging and enhanced scoring metrics, with updates to sample code and support for Chinese inputs. No major bugs fixed this month. Business value: improved speech quality evaluation, telemetry, and localization support.
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