使用1D-CNN进行文学与口语泰米尔语语音分类的特征工程方法
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN
AI总结：
本文提出一种基于1D-CNN的轻量级分类器，通过结合手工特征与MFCC特征，有效区分文学与口语泰米尔语语音，特征组合后取得0.9946的F1分数。
摘要：
在理想的人机交互（HCI）中，大多数用户会更倾向于使用语言的口语形式，因为这是他们日常交流中使用的形式。然而，保护正式的文学形式也是不可忽视的必要需求。通过拥抱新事物并保留旧传统，既能服务于普通人（实用性），又能服务于语言本身（保护）。因此，理想的情况是计算机能够根据需要，接受、处理并用这两种语言形式进行交流。为了解决这个问题，首先需要识别输入语音的形式，在当前工作中，这是在文学泰米尔语和口语泰米尔语之间进行的区分。这样的前端系统必须包含一个简单、有效且轻量级的分类器，该分类器基于少数能够捕捉语音信号潜在模式的有效特征进行训练。为此，提出了一种一维卷积神经网络（1D-CNN），用于学习特征随时间变化的包络。该网络最初在选定数量的手工特征上进行训练，随后在Mel频率倒谱系数（MFCC）上进行训练以作比较。选择手工特征是为了处理语音的各个方面，如频谱和时间特征、韵律以及声音质量。最初通过考虑十个平行话语并观察每个特征随时间变化的趋势来分析这些特征。所提出的1D-CNN在使用手工特征训练时，F1分数为0.9803，而在使用MFCC训练时，F1分数为0.9895。鉴于此，本文探讨了特征消融和特征组合。当将特征消融研究中排名最高的手工特征与MFCC结合时，获得了最佳结果，F1分数为0.9946。
英文摘要：
In ideal human computer interaction (HCI), the colloquial form of a language would be preferred by most users, since it is the form used in their day-to-day conversations. However, there is also an undeniable necessity to preserve the formal literary form. By embracing the new and preserving the old, both service to the common man (practicality) and service to the language itself (conservation) can be rendered. Hence, it is ideal for computers to have the ability to accept, process, and converse in both forms of the language, as required. To address this, it is first necessary to identify the form of the input speech, which in the current work is between literary and colloquial Tamil speech. Such a front-end system must consist of a simple, effective, and lightweight classifier that is trained on a few effective features that are capable of capturing the underlying patterns of the speech signal. To accomplish this, a one-dimensional convolutional neural network (1D-CNN) that learns the envelope of features across time, is proposed. The network is trained on a select number of handcrafted features initially, and then on Mel frequency cepstral coefficients (MFCC) for comparison. The handcrafted features were selected to address various aspects of speech such as the spectral and temporal characteristics, prosody, and voice quality. The features are initially analyzed by considering ten parallel utterances and observing the trend of each feature with respect to time. The proposed 1D-CNN, trained using the handcrafted features, offers an F1 score of 0.9803, while that trained on the MFCC offers an F1 score of 0.9895. In light of this, feature ablation and feature combination are explored. When the best ranked handcrafted features, from the feature ablation study, are combined with the MFCC, they offer the best results with an F1 score of 0.9946.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
