语音语料库_part_14 TEST DR6 TIMIT contains a total of 6300 sentences, 10 sentences spoken by each of 630 speakers from 8 major dialect regions of the United States. Table 1 shows the number of speakers for the 8 dialect regions, broken down by sex. The percentages are given in parentheses. A speaker's dialect region is the geographical area of the U.S. where they lived during their childhood years.

语音语料库_part_15 TEST DR7 TIMIT contains a total of 6300 sentences, 10 sentences spoken by each of 630 speakers from 8 major dialect regions of the United States. Table 1 shows the number of speakers for the 8 dialect regions, broken down by sex. The percentages are given in parentheses. A speaker's dialect region is the geographical area of the U.S. where they lived during their childhood years.

语音语料库_part_16 TEST DR8 TIMIT contains a total of 6300 sentences, 10 sentences spoken by each of 630 speakers from 8 major dialect regions of the United States. Table 1 shows the number of speakers for the 8 dialect regions, broken down by sex. The percentages are given in parentheses. A speaker's dialect region is the geographical area of the U.S. where they lived during their childhood years.

语音语料库_part_17 README TIMIT contains a total of 6300 sentences, 10 sentences spoken by each of 630 speakers from 8 major dialect regions of the United States. Table 1 shows the number of speakers for the 8 dialect regions, broken down by sex. The percentages are given in parentheses. A speaker's dialect region is the geographical area of the U.S. where they lived during their childhood years.

能过实现基本的语音识别功能，内部代码含有注释，适合初学者，对入门有一定帮助。

delphi源码, Microsoft Speech SDK5.4语音处理, Delphi2007测试通过, 有demo.exe.

包含speech sdk 5.1和speech sdk 5.1 langpack

本程序可以完成对以arpa语言模型文件格式保存的语言模型的建立，性能评估，包括如何计算交叉熵，如何计算困惑度perplexity.

激光 闪电自动语音识别 一个基于PyTorch-Lightning的MIT许可ASR研究库，用于开发端到端ASR模型。 介绍 是用于高性能AI研究的轻量级包装器。 PyTorch非常易于使用，可以构建复杂的AI模型。 但是一旦研究变得复杂，并且诸如多GPU训练，16位精度和TPU训练之类的东西混在一起，用户很可能会引入错误。 PyTorch Lightning恰好解决了这个问题。 Lightning构造了您的PyTorch代码，因此它可以抽象出培训的详细信息。 这使AI研究具有可扩展性，并且可以快速迭代。 该项目是使用PyTorch Lightning实现asr项目的示例。 在这个项目中，我训练了一个模型，该模型包括一个带有联合CTC注意的构象编码器+ LSTM解码器。 所述LASR装置升ighthning一个utomatic小号peechřecognition。 我希望这可以成为那些研究

lpc matlab代码语音压缩 Runnig代码 按原样下载所有文件，然后在Matlab上运行.mlapp文件。 线性预测编码 将人类语音建模为过去样本的线性函数。 在MATLAB App的帮助下创建了UI。