“音素分割作为语音处理领域内的一个重要任务,对于关键词识别、自动语音识别等应用具有至关重要的意义。传统方法往往独立预测每一帧音频是否为音素边界,忽视了音素边界与整个音频序列以及相邻帧之间的内在联系,从而影响了分割的准确性和连贯性。本文提出一种基于预训练模型与序列建模的音素分割方法,在HuBERT模型提取声学特征的基础上,结合BiLSTM捕捉长期依赖,再用CRF优化序列,提升了音素边界检测的性能。在TIMIT和Buckeye数据集上的实验表明,本文方法优于现有技术,证明了序列建模在音素分割任务中的有效性。”

“Dialect speech recognition has always been one of the challenges in Automatic Speech Recog-nition (ASR) systems. While lots of ASR systems perform well in Mandarin, their performancesignificantly drops when handling dialect speech. This is mainly due to the obvious differencesbetween dialects and Mandarin in pronunciation and the data scarcity of dialect speech. In thispaper, we propose DialectMoE, a Chinese multi-dialects speech recognition model based onMixture-of-Experts (MoE) in a low-resource conditions. Specifically, DialectMoE assigns inputsequences to a set of experts using a dynamic routing algorithm, with each expert potentiallytrained for a specific dialect. Subsequently, the outputs of these experts are combined to derivethe final output. Due to the similarities among dialects, distinct experts may offer assistance inrecognizing other dialects as well. Experimental results on the Datatang dialect public datasetshow that, compared with the baseline model, DialectMoE reduces Character Error Rate (CER)for Sichuan, Yunnan, Hubei and Henan dialects by 23.6%, 32.6%, 39.2% and 35.09% respec-tively. The proposed DialectMoE model demonstrates outstanding performance in multi-dialectsspeech recognition.”