Develop an x-vector system to perform speaker recognition.

Speaker diarization is the process of partitioning an audio signal into segments according to speaker identity. It answers the question "who spoke when" without prior knowledge of the speakers and, depending on the application, without prior knowledge of the number of speakers.

Trains a spoken digit recognition network on out-of-memory auditory spectrograms using a transformed datastore. In this example, you extract auditory spectrograms from audio using audioDatastore and audioFeatureExtractor, and you write them to disk. You then use a signalDatastore to access the features during training. The workflow is useful when the training features do not fit in memory. In this workflow, you only extract features once, which speeds up your workflow if you are iterating on the deep learning model design.

使用变换的数据存储列在内存失控的音频数据上进行口头识别网络。在此示例中，您将随机间距移位应用于用于训练卷积神经网络（CNN）的音频数据。对于每个训练迭代，使用audiodataAugmenter对象增强音频数据，然后使用audiofeatureextractor对象提取功能。此示例中的工作流程适用于训练循环中使用的任何随机数据增强。工作流程也适用于底层的音频数据集或培训功能不适合内存时。

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a given set of commands.

使用深度学习检测低信噪比环境中的语音区域。该示例使用语音命令DataSet培训双向短期内存（BILSTM）网络以检测语音活动。

使用机器和深度学习技术进行语音数字。在该示例中，使用使用支持向量机（SVM）和长短期存储器（LSTM）网络进行小波时间散射进行分类。金宝app您还应用贝叶斯优化以确定合适的超参数以提高LSTM网络的准确性。另外，该示例说明了使用深卷积神经网络（CNN）和熔融频谱图的方法。

Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore. In wavelet scattering, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of the data. These low-variance representations are then used as inputs to a classifier.

适用于特征选择的典型工作流程应用于口头识别的任务。

为声学场景识别创建多模型后融合系统。该示例使用MEL谱图和使用小波散射的集合分类器列举卷积神经网络（CNN）。该示例使用TUT数据集进行培训和评估[1]。

演讲者验证或认证,是助教sk of confirming that the identity of a speaker is who they purport to be. Speaker verification has been an active research area for many years. An early performance breakthrough was to use a Gaussian mixture model and universal background model (GMM-UBM) [1] on acoustic features (usually mfcc). For an example, see Speaker Verification Using Gaussian Mixture Models. One of the main difficulties of GMM-UBM systems involves intersession variability. Joint factor analysis (JFA) was proposed to compensate for this variability by separately modeling inter-speaker variability and channel or session variability [2] [3]. However, [4] discovered that channel factors in the JFA also contained information about the speakers, and proposed combining the channel and speaker spaces into a total variability space. Intersession variability was then compensated for by using backend procedures, such as linear discriminant analysis (LDA) and within-class covariance normalization (WCCN), followed by a scoring, such as the cosine similarity score. [5] proposed replacing the cosine similarity scoring with a probabilistic LDA (PLDA) model. [11] and [12] proposed a method to Gaussianize the i-vectors and therefore make Gaussian assumptions in the PLDA, referred to as G-PLDA or simplified PLDA. While i-vectors were originally proposed for speaker verification, they have been applied to many problems, like language recognition, speaker diarization, emotion recognition, age estimation, and anti-spoofing [10]. Recently, deep learning techniques have been proposed to replace i-vectors with d-vectors or x-vectors [8] [6].

扬声器验证或身份验证是验证给定语音段属于给定扬声器的任务。在扬声器验证系统中，有一套未知的所有其他扬声器，所以话语属于验证目标的可能性被比较了它的可能性。这与扬声器识别任务形成鲜明对比，其中计算每个扬声器的可能性，并且比较这些似然性。发言者验证和扬声器标识都可以是文本依赖或文本。在此示例中，您使用高斯混合模型/通用背景模型（GMM-UBM）创建文本相关的扬声器验证系统。

Perform pitch tracking using multiple pitch estimations, octave and median smoothing, and a hidden Markov model (HMM).

Use the Levinson-Durbin and Time-Varying Lattice Filter blocks for low-bandwidth transmission of speech using linear predictive coding.

使用实现MEL级滤波器组的自定义深度学习层执行语音识别。