Blog(7)

• pMCT - Patched Multi-Condition Training

  The clashing of pans and pots as you cook and ask your voice assistant what you can use to replace eggs in the recipe. The excited, overlapping conversations as you ask which of Henry the VIIIs wives survived, trying to settle a bet.

• [INTERSPEECH 2022 Series #6] Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition

  Automatic Speech Recognition (ASR) systems on smart devices have traditionally relied on server based models. This involves sending audio data to the server and receiving text hypothesis once the server model completes decoding.

• [INTERSPEECH 2022 Series #5] Prototypical Speaker-Interference Loss for Target Voice Separation Using Non-Parallel Audio Samples

  Deep learning based audio signal processing is increasingly becoming popular for solving the cocktail party problem [1-5]. A noisy signal, which is mixed by a clean and noise signal, is used as a pair with the clean signal for training a speech enhancement or source separation model [6-7].

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News(7)

• [Blog] [INTERSPEECH 2022 Series #6] Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition

  Automatic Speech Recognition (ASR) systems on smart devices have traditionally relied on server based models. This involves sending audio data to the server and receiving text hypothesis once the server model completes decoding.

• [Blog] [INTERSPEECH 2022 Series #5] Prototypical Speaker-Interference Loss for Target Voice Separation Using Non-Parallel Audio Samples

  Deep learning based audio signal processing is increasingly becoming popular for solving the cocktail party problem [1-5]. A noisy signal, which is mixed by a clean and noise signal, is used as a pair with the clean signal for training a speech enhancement or source separation model [6-7].

• [Blog] [INTERSPEECH 2022 Series #4] Cross-Modal Decision Regularization for Simultaneous Speech Translation

  In today’s world of virtual meetings, conferences, and multi-media, automatic speech translation offers a wide variety of applications. Traditional offline speech translation models used a cascade of speech recognition and text translation. In our prior works [1], we developed efficient techniques for end-to-end speech translation which outperforms traditional cascaded approaches.

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