Microsoft Speech SDK 5.1的安装与使用，详解

Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

循环神经网络在语音识别中的应用 LSTM 双向RNN 双向lstm

子带滤波方面的经典书籍，相关专业的学生和从业者都可以将本书作为参考资料。

True Speech声音解码器

This paper describes a method for enhancing speech corrupted by broadband noise. The method is based on the spectral noise subtraction method. The original method entails subtracting an estimate of the noise power spectrum from the speech power spectrum, setting negative differences to zero

新增一个直接可以通过预训练模型训练任何的数据集的代码叫traindecoder 这样就可以任何的数据集都可以使用任何的数据集的预训练模型，节约训练时间

Microsoft Speech SDK安装包及博客教程（语音识别）

音频深度学习（DLA） 每周的讲座和研讨会资料位于./week*文件夹中，有关资料和说明，请参阅README.md。 任何技术问题，想法，课程资料中的错误，贡献想法-添加问题 该课程的当前版本于2020年秋季在的进行 教学大纲 数字信号处理简介 讲座：信号，傅立叶变换，频谱图，MFCC等 研讨会：PyTorch简介，DevOps，深度学习研发 自动语音识别I 讲座：指标，注意力，LAS，CTC，BeamSearch 研讨会：Docker，W＆B，音频增强 自动语音识别II 演讲：LM融合，RNN传感器，进度表采样，BPE 研讨会：Jasper，QurtzNet，混合精度培训，DDP / DP 关键字（KWS）和语音活动检测（VAD） 演讲：（DNN，CNN，RNN + Attention）基于KWS，SVDF，正交正则化和其他技巧 研讨会：加速神经网络：张量分解，量化，修剪