A recent trend in speech processing is the use of embeddings created through machine learning models trained on a specific task with large datasets. By leveraging the knowledge already acquired, these models can be reused in new tasks where the amount of available data is small. This paper proposes a pipeline to create a new model, called Mel and Wave Embeddings for Human Voice Tasks (MeWEHV), capable of generating robust embeddings for speech processing. MeWEHV combines the embeddings generated by a pre-trained raw audio waveform encoder model, and deep features extracted from Mel Frequency Cepstral Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate the performance of MeWEHV on three tasks: speaker, language, and accent identification. For the first one, we use the VoxCeleb1 dataset and present YouSpeakers204, a new and publicly available dataset for English speaker identification that contains 19607 audio clips from 204 persons speaking in six different accents, allowing other researchers to work with a very balanced dataset, and to create new models that are robust to multiple accents. For evaluating the language identification task, we use the VoxForge and Common Language datasets. Finally, for accent identification, we use the Latin American Spanish Corpora (LASC) and Common Voice datasets. Our approach allows a significant increase in the performance of state-of-the-art models on all the tested datasets, with a low additional computational cost.

翻译：近期语音处理领域的一个趋势是使用通过大规模数据集在特定任务上训练的机器学习模型生成的嵌入。通过利用已习得的知识，这些模型可被复用于数据量有限的新任务中。本文提出了一种流水线方法，用于创建名为“面向人声任务的梅尔与波形嵌入（MeWEHV）”的新模型，该模型能够生成鲁棒的语音处理嵌入。MeWEHV结合了预训练原始音频波形编码器模型生成的嵌入，以及通过卷积神经网络（CNN）从梅尔频率倒谱系数（MFCC）中提取的深层特征。我们在说话人识别、语种识别和口音识别三项任务上评估了MeWEHV的性能。对于第一项任务，我们使用VoxCeleb1数据集，并提出YouSpeakers204——一个全新的、公开可用的英语说话人识别数据集，包含来自204位使用六种不同口音发言者的19607个音频片段，使其他研究者能够使用高度均衡的数据集，并构建对多口音鲁棒的新模型。为评估语种识别任务，我们使用VoxForge和Common Language数据集。最后，对于口音识别，我们使用拉丁美洲西班牙语语料库（LASC）和Common Voice数据集。我们的方法在所有测试数据集上均能显著提升现有最优模型的性能，且额外计算成本较低。