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Performance analysis of hybrid model of robust automatic continuous speech recognition system

机译：鲁棒自动连续语音识别系统混合模型的性能分析

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摘要

In this work, we evaluate the performance of objective measures of noisy input of continuous speech signal through the Hybrid method using Voice Activity Detection (VAD) and Speech Enhancement Algorithm (SEA). Automatic Speech Recognition (ASR) is an important technology, which enables natural human-machine interaction, for over five decades. The objective of this work consists in working out an identification of continuous speech recognition. The methodology presented allows evaluating the process which includes a speech-to-text system using continuous word recognition with a vocabulary of ten words (digits 0 to 9). In the training period, the continuous digits are recorded using 8-bit Pulse Code Modulation (PCM) with a sampling rate of 8 KHz and save as a wave format file using sound recorder software. For a given word in the vocabulary, the system builds an Hidden Markov Model (HMM) model and trains the model during the training phase. The training steps, from VAD, Speech Enhancement to HMM model building, are performed using PC-based Matlab programs. An overall Recognition Accuracy (RA) of 72.45% is achieved from the proposed speech recognition system working under different environment condition for an uttered word.
机译：在这项工作中，我们通过使用语音活动检测（VAD）和语音增强算法（SEA）的混合方法评估连续语音信号的噪声输入的客观测量的性能。自动语音识别（ASR）是一项重要的技术，可以实现自然的人机交互长达五十年之久。这项工作的目的在于确定连续语音识别。提出的方法可以评估包括语音到文本系统的过程，该系统使用具有十个单词（0到9位数字）的词汇的连续单词识别。在训练期间，使用8位脉冲编码调制（PCM）以8 KHz的采样率记录连续数字，并使用录音机软件将其保存为波形文件。对于词汇表中的给定单词，系统会建立一个隐马尔可夫模型（HMM）模型，并在训练阶段训练该模型。使用基于PC的Matlab程序执行从VAD，语音增强到HMM模型构建的培训步骤。通过所提出的语音识别系统在不同环境条件下对一个说出的单词工作，可以实现72.45％的整体识别准确度（RA）。

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