Standard Yorùbá context dependent tone identification using Multi-Class Support Vector Machine (MSVM)

Most state-of-the-art large vocabulary continuous speech recognition systems employ context dependent (CD) phone units, however, the CD phone units are not efficient in capturing long-term spectral dependencies of tone in most tone languages. The Standard Yorùbá (SY) is a language composed of syllable with tones and requires different method for the acoustic modeling. In this paper, a context dependent tone acoustic model was developed. Tone unit is assumed as syllables, amplitude magnified difference function (AMDF) was used to derive the utterance wide F contour, followed by automatic syllabification and tri-syllable forced alignment with speech phonetization alignment and syllabification SPPAS tool. For classification of the context dependent (CD) tone, slope and intercept of F values were extracted from each segmented unit. Supervised clustering scheme was utilized to partition CD tri-tone based on category and normalized based on some statistics to derive the acoustic feature vectors. Multi-class support vector machine (MSVM) was used for tri-tone training. From the experimental results, it was observed that the word recognition accuracy obtained from the MSVM tri-tone system based on dynamic programming tone embedded features was comparable with phone features. A best parameter tuning was obtained for 10-fold cross validation and overall accuracy was 97.5678%. In term of word error rate (WER), the MSVM CD tri-tone system outperforms the hidden Markov model tri-phone system with WER of 44.47%.Keywords: Syllabification, Standard Yorùbá, Context Dependent Tone, Tri-tone Recognitio

Improved language identification using deep bottleneck network

Effective representation plays an important role in automatic spoken language identification (LID). Recently, several representations that employ a pre-trained deep neural network (DNN) as the front-end feature extractor, have achieved state-of-the-art performance. However the performance is still far from satisfactory for dialect and short-duration utterance identification tasks, due to the deficiency of existing representations. To address this issue, this paper proposes the improved representations to exploit the information extracted from different layers of the DNN structure. This is conceptually motivated by regarding the DNN as a bridge between low-level acoustic input and high-level phonetic output features. Specifically, we employ deep bottleneck network (DBN), a DNN with an internal bottleneck layer acting as a feature extractor. We extract representations from two layers of this single network, i.e. DBN-TopLayer and DBN-MidLayer. Evaluations on the NIST LRE2009 dataset, as well as the more specific dialect recognition task, show that each representation can achieve an incremental performance gain. Furthermore, a simple fusion of the representations is shown to exceed current state-of-the-art performance