Hierarchical Multilayer Perceptron based Language Identification

Automatic language identification (LID) systems generally exploit acoustic knowledge, possibly enriched by explicit language specific phonotactic or lexical constraints. This paper investigates a new LID approach based on hierarchical multilayer perceptron (MLP) classifiers, where the first layer is a "universal phoneme set MLP classifier''. The resulting (multilingual) phoneme posterior sequence is fed into a second MLP taking a larger temporal context into account. The second MLP can learn/exploit implicitly different types of patterns/information such as confusion between phonemes and/or phonotactics for LID. We investigate the viability of the proposed approach by comparing it against two standard approaches which use phonotactic and lexical constraints with the universal phoneme set MLP classifier as emission probability estimator. On SpeechDat(II) datasets of five European languages, the proposed approach yields significantly better performance compared to the two standard approaches

Text based user comments as a signal for automatic language identification of online videos

Identifying the audio language of online videos is crucial for industrial multi-media applications. Automatic speech recognition systems can potentially detect the language of the audio. However, such systems are not available for all languages. Moreover, background noise, music and multi-party conversations make audio language identification hard. Instead, we utilize text based user comments as a new signal to identify audio language of YouTube videos. First, we detect the language of the text based comments. Augmenting this information with video meta-data features, we predict the language of the videos with an accuracy of 97% on a set of publicly available videos. The subject matter discussed in this research is patent pending

Development of Bilingual ASR System for MediaParl Corpus

The development of an Automatic Speech Recognition (ASR) system for the bilingual MediaParl corpus is challenging for several reasons: (1) reverberant recordings, (2) accented speech, and (3) no prior information about the language. In that context, we employ frequency domain linear prediction-based (FDLP) features to reduce the effect of reverberation, exploit bilingual deep neural networks applied in Tandem and hybrid acoustic modeling approaches to significantly improve ASR for accented speech and develop a fully bilingual ASR system using entropy-based decoding-graph selection. Our experiments indicate that the proposed bilingual ASR system performs similar to a language-specific ASR system if approximately five seconds of speech are available