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July 30, 20268 min readInstead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically.
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July 9, 202610 min read
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Featured news
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Interspeech 20202020A modern Spoken Language Understanding (SLU) system usually contains two sub-systems, Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU),where ASR transforms voice signal to text form and NLU provides intent classification and slot filling from the text. In practice,such decoupled ASR/NLU design facilitates fast model iteration for both components. However, this makes downstream NLU
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Interspeech 20202020Prosody Transfer (PT) is a technique that aims to use the prosody from a source audio as a reference while synthesizing speech. Fine-grained PT aims at capturing prosodic aspects like rhythm, emphasis, melody, duration, and loudness, from a source audio at a very granular level and transferring them when synthesizing speech in a different target speaker’s voice. Cur-rent approaches for fine-grained PT suffer
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Interspeech 20202020The speech recognition training data corresponding to digital voice assistants is dominated by wake-words. Training endto-end (E2E) speech recognition models without careful attention to such data results in sub-optimal performance as models prioritize learning wake-words. To address this problem, we propose a novel discriminative initialization strategy by introducing a regularization term to penalize
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Interspeech 20202020As a crucial part of Alexa products, our on-device keyword spotting system detects the wakeword in conversation and initiates subsequent user-device interactions. Convolutional neural networks (CNNs) have been widely used to model the relationship between time and frequency in the audio spectrum. However, it is not obvious how to appropriately leverage the rich descriptive information from device state
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Interspeech 20202020Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns. For example, a cooking scene may contain several sound sources including silverware clinking, chopping, frying, etc. What complicates ASC more is that classes of different activities could have overlapping sounds patterns (e.g. both cooking and dishwashing could have
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