Language detection in audio content analysis
Vikramjit Mitral, Daniel Garcia-Romero, Carol Espy-Wilson · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
Experiments have shown that Language Identification systems for telephonic speech using shifted delta cepstra as the feature set and Gaussian mixture models as the backend, offers superior performance than other competing techniques. This paper aims to address the task of Language Identification for audio signals. The abundance of digital music from the Internet calls for a reliable real-time system for analyzing and properly categorizing them. Previous research has mainly focused on categorizing audio files into appropriate genres; however genre types vary with language. This paper proposes a systematic audio content analysis strategy by initially detecting whether an audio file has any vocals present in it and, if present, then detecting the language of the song. Given the language of the song, genre detection becomes a closed set classification problem.