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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Combining Acoustic Embeddings and Decoding Features for End-of-Utterance Detection in Real-Time Far-Field Speech Recognition Systems
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Combining Acoustic Embeddings and Decoding Features for End-of-Utterance Detection in Real-Time Far-Field Speech Recognition Systems

机译：组合声学嵌入和解码特征在实时远场语音识别系统中的话语末端检测

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摘要

We present an end-of-utterance detector for real-time automatic speech recognition in far-field scenarios. The proposed system consists of three components: a long short-term memory (LSTM) neural network trained on acoustic features, an LSTM trained on l-best recognition hypotheses of the automatic speech recognition (ASR) decoder, and a feedforward deep neural network (DNN) combining embeddings derived from both LSTMs with pause duration features from the ASR decoder. At inference time, lower and upper latency (pause duration) bounds act as safeguards. Within the latency bounds, the utterance end-point is triggered as soon as the DNN posterior reaches a tuned threshold. Our experimental evaluation is carried out on real recordings of natural human interactions with voice-controlled far-field devices. We show that the acoustic embeddings are the single most powerful feature and particularly suitable for cross-lingual applications. We furthermore show the benefit of ASR decoder features, especially as a low cost alternative to ASR hypothesis em-beddings.
机译：我们在远场情景中呈现出用于实时自动语音识别的话语终止探测器。所提出的系统由三个组成部分组成：长期内存（LSTM）神经网络训练在声学特征上，LSTM培训于自动语音识别（ASR）解码器的L-BEST识别假设，以及前馈深神经网络（ DNN）组合从SSTMS派生的嵌入源与ASR解码器的暂停持续时间特征。在推理时间内，较低和上延迟（暂停持续时间）界限充当保护。在延迟界限内，一旦DNN后续到达调谐阈值，就会触发话语终点。我们的实验评估是在与语音控制的远场设备的自然人交互的真正记录中进行的。我们表明声学嵌入式是最强大的功能，特别适用于交叉舌应用。我们还展示了ASR解码器特征的好处，特别是作为ASR假设EM-BEDDINGS的低成本替代品。

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