In recent years, the standard hybrid DNN-HMM speech recognizers are
outperformed by the end-to-end speech recognition systems. One of the very
promising approaches is the grapheme Wav2Vec 2.0 model, which uses the
self-supervised pretraining approach combined with transfer learning of the
fine-tuned speech recognizer. Since it lacks the pronunciation vocabulary and
language model, the approach is suitable for tasks where obtaining such models
is not easy or almost impossible.
In this paper, we use the Wav2Vec speech recognizer in the task of spoken
term detection over a large set of spoken documents. The method employs a deep
LSTM network which maps the recognized hypothesis and the searched term into a
shared pronunciation embedding space in which the term occurrences and the
assigned scores are easily computed.
The paper describes a bootstrapping approach that allows the transfer of the
knowledge contained in traditional pronunciation vocabulary of DNN-HMM hybrid
ASR into the context of grapheme-based Wav2Vec. The proposed method outperforms
the previously published system based on the combination of the DNN-HMM hybrid
ASR and phoneme recognizer by a large margin on the MALACH data in both English
and Czech languages.

本文利用基于字符的 Wav2Vec2.0 模型进行了大规模口语文档中的术语检测任务，实验结果表明，使用基于深度 LSTM 网络的共享发音嵌入空间的 bootstrapping 方法将 DNN-HMM 混合 ASR 的传统发音词汇的知识转化到基于图形的 Wav2Vec 中，能够显著优于 DNN-HMM 混合 ASR 和音素识别器的组合系统。