24 research outputs found

    CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR

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    History and future contextual information are known to be important for accurate acoustic modeling. However, acquiring future context brings latency for streaming ASR. In this paper, we propose a new framework - Chunking, Simulating Future Context and Decoding (CUSIDE) for streaming speech recognition. A new simulation module is introduced to recursively simulate the future contextual frames, without waiting for future context. The simulation module is jointly trained with the ASR model using a self-supervised loss; the ASR model is optimized with the usual ASR loss, e.g., CTC-CRF as used in our experiments. Experiments show that, compared to using real future frames as right context, using simulated future context can drastically reduce latency while maintaining recognition accuracy. With CUSIDE, we obtain new state-of-the-art streaming ASR results on the AISHELL-1 dataset.Comment: submitted to INTERSPEECH 202

    End-to-End Simultaneous Speech Translation

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    Speech translation is the task of translating speech in one language to text or speech in another language, while simultaneous translation aims at lower translation latency by starting the translation before the speaker finishes a sentence. The combination of the two, simultaneous speech translation, can be applied in low latency scenarios such as live video caption translation and real-time interpretation. This thesis will focus on an end-to-end or direct approach for simultaneous speech translation. We first define the task of simultaneous speech translation, including the challenges of the task and its evaluation metrics. We then progressly introduce our contributions to tackle the challenges. First, we proposed a novel simultaneous translation policy, mono- tonic multihead attention, for transformer models on text-to-text translation. Second, we investigate the issues and potential solutions when adapting text-to-text simultaneous policies to end-to-end speech-to-text translation models. Third, we introduced the augmented memory transformer encoder for simultaneous speech-to-text translation models for better computation efficiency. Fourth, we explore a direct simultaneous speech translation with variational monotonic multihead attention policy, based on recent speech-to-unit models. At the end, we provide some directions for potential future research

    Self-Attention Transducers for End-to-End Speech Recognition

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    Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelization . In this paper, we propose a self-attention transducer (SA-T) for speech recognition. RNNs are replaced with self-attention blocks, which are powerful to model long-term dependencies inside sequences and able to be efficiently parallelized. Furthermore, a path-aware regularization is proposed to assist SA-T to learn alignments and improve the performance. Additionally, a chunk-flow mechanism is utilized to achieve online decoding. All experiments are conducted on a Mandarin Chinese dataset AISHELL-1. The results demonstrate that our proposed approach achieves a 21.3% relative reduction in character error rate compared with the baseline RNN-T. In addition, the SA-T with chunk-flow mechanism can perform online decoding with only a little degradation of the performance

    Fast-U2++: Fast and Accurate End-to-End Speech Recognition in Joint CTC/Attention Frames

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    Recently, the unified streaming and non-streaming two-pass (U2/U2++) end-to-end model for speech recognition has shown great performance in terms of streaming capability, accuracy and latency. In this paper, we present fast-U2++, an enhanced version of U2++ to further reduce partial latency. The core idea of fast-U2++ is to output partial results of the bottom layers in its encoder with a small chunk, while using a large chunk in the top layers of its encoder to compensate the performance degradation caused by the small chunk. Moreover, we use knowledge distillation method to reduce the token emission latency. We present extensive experiments on Aishell-1 dataset. Experiments and ablation studies show that compared to U2++, fast-U2++ reduces model latency from 320ms to 80ms, and achieves a character error rate (CER) of 5.06% with a streaming setup.Comment: 5 pages, 3 figure
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