In this paper, an automatic dialect identification (ADI) system is proposed by extracting spectral and prosodic features for Kannada language. A new dialect dataset is collected from native speakers of Kannada language (A Dravidian language). This dataset includes five distinct dialects of Kannada language representing five geographical regions of Karnataka state. Investigation of the significance of spectral and prosodic variations on five Kannada dialects is carried out. Mel-frequency cepstral coefficients (MFCCs), spectral flux, and entropy are used as representatives of spectral features. Besides, pitch and energy features are extracted as representatives of prosodic parameters for identification of dialects. These raw feature vectors are further processed to get a new derived feature vectors by using statistical processing. In this paper, a single classifier based multi-class support vector machine (SVM) and multiple classifier based ensemble SVM (ESVM) techniques are employed for classification of dialects. The effectiveness and performance evaluation of the explored features are carried out on newly collected Kannada speech corpus, with five Kannada dialects and internationally known standard Intonation Variation in English (IViE) dataset with nine British English dialects. Experimental results have demonstrated that the derived feature vectors performs better when compared to raw feature vectors. However, ESVM technique has demonstrated better performance over a single SVM. Spectral and prosodic features have resulted individually with the dialect recognition performance of 83.12% and 44.52% respectively. Further, the complementary nature of both spectral and prosodic features is evaluated by combining both feature vectors for dialect recognition. However, an increase in dialect recognition performance of about 86.25% is observed. This indicates the existence of complementary dialect specific evidence with spectral and prosodic features. The experiments conducted on standard IViE corpus have shown a higher recognition rate of 91.38% using ESVM. Proposed ADI systems with derived features have shown better performance over the state-of-the-art i-vector feature based systems on both datasets.

中文翻译：

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使用单一和集成SVM算法的卡纳达语语言自动方言识别系统

通过提取卡纳达语的频谱特征和韵律特征，提出了一种自动方言识别（ADI）系统。从卡纳达语（德拉维语）的母语使用者那里收集了一个新的方言数据集。该数据集包括代表卡纳塔克邦五个地理区域的卡纳达语的五个不同方言。对五个卡纳达语方言的频谱和韵律变化的重要性进行了研究。梅尔频率倒谱系数（MFCC），频谱通量和熵用作频谱特征的代表。此外，音调和能量特征被提取为用于识别方言的韵律参数的代表。通过使用统计处理，对这些原始特征向量进行进一步处理以获得新的派生特征向量。在本文中，基于单分类器的多分类支持向量机（SVM）和基于多分类器的集成SVM（ESVM）技术被用于方言分类。在新收集的卡纳达语语音语料库上使用五种卡纳达语方言和国际上已知的标准英语音调变化（IViE）数据集与九种英国英语方言进行了探索特征的有效性和性能评估。实验结果表明，与原始特征向量相比，导出的特征向量表现更好。但是，ESVM技术已显示出比单个SVM更好的性能。分别产生了谱和韵律特征，方言识别率分别为83.12％和44.52％。进一步，频谱特征和韵律特征的互补性质是通过结合两种特征向量进行方言识别来评估的。但是，观察到方言识别性能提高了约86.25％。这表明存在具有频谱和韵律特征的互补方言特定证据。使用ESVM在标准IViE语料库上进行的实验显示出更高的识别率91.38％。与两个数据集上基于最新i-vector特征的系统相比，拟议的具有衍生特征的ADI系统表现出更好的性能。使用ESVM在标准IViE语料库上进行的实验显示出更高的识别率91.38％。与两个数据集上基于最新i-vector特征的系统相比，拟议的具有衍生特征的ADI系统表现出更好的性能。使用ESVM在标准IViE语料库上进行的实验显示出更高的识别率91.38％。与两个数据集上基于最新i-vector特征的系统相比，拟议的具有衍生特征的ADI系统表现出更好的性能。