基于自回归预训练语言模型的语音信号关键词提取方法

Key Word Extraction Method for Speech Signals Based on Autoregressive Pretrained Language Model

常规语音信号关键词提取多采用图神经网络算法,通过关键信息的特征向量表示实现关键词提取,但此方法由于缺少对语音信号尺度特征分量的识别,导致最终关键词提取质量不佳.为此,提出基于自回归预训练语言模型的语音信号关键词提取方法.以语音信号在时域内的变化波形为依据,对信号进行去噪,并通过对信号进行多模态分解以获取尺度特征分量,由此构建语音信号词向量特征,结合聚类算法求取全段语音信号的语义向量参数,并对初选关键词词义进行去重,以此为依据,引入自回归预训练语言模型计算候选关键词与语音信号语义向量的相似度,进而实现语音信号的关键词提取.实验结果表明,在关键词数量为5~30个范围内,所提方法的提取召回率始终保持在80%以上.所提方法能够有效提升语音信号关键词的提取质量,实现简便,可广泛应用于语音信号处理领域.

Conventional speech signal keyword extraction often uses graph neural network algorithms,which achieves keyword extraction through the representation of key information feature vectors.However,this method lacks recognition of speech signal scale feature components,resulting in poor quality of final keyword extraction.A speech signal keyword extraction method based on autoregressive pre trained language model is proposed.Based on the waveform changes of the speech signal in the time domain,the signal is denoised,and the scale feature components are obtained by multi-modal decomposition of the signal.From this,the word vector features of the speech signal are constructed,and the semantic vector parameters of the entire speech signal are obtained by combining clustering algorithms.The semantic vector parameters of the initially selected keywords are then deduplicated.An autoregressive pre trained language model is introduced to calculate the similarity between candidate keywords and semantic vectors of speech signals.The keyword extraction of speech signals are achieved.The experimental results show that within the range of 5~30 keywords,the extraction recall of the proposed method remains above 80%.The proposed method can effectively improve the quality of extracting keywords from speech signals,is easy to implement,and can be widely applied in the field of speech signal processing.