Automatic Language Identification Using Inductive Inference

Automatic spoken language identification (LID) plays an important part in routing foreign callers to operators who speak the caller's language, or as a front-end to a multi-lingual translation system to route the call to the appropriate translation system. A common approach to spoken language ID is adopted from current speaker independent recognition techniques. These generally involve the development of a phonetic recognisor for each language and then combining the acoustic likelihood scores to determine the highest scoring language. The models are trained using hidden Markov modelling (HMM) or neural networks (NN). This paper proposes a novel approach to spoken language identification by the use of inductive inference "decision trees". To develop the production rules, the classification models are generated inductively by examining a large speech database and then generalising the pattern from the specific examples. This approach has already been successfully used for isolated digit recognition (Samouelian, 1996). The aim of this research is to demonstrate that inductive learning can provide a viable alternative approach to existing automatic spoken language identification techniques. The proposed LID is based on automatic speech recognition (ASR) system using inductive inference (Samouelian, 1994a, 1994b). It uses a single decision tree to capture all the complexities of each language, using mel-scaled cepstral coefficients (MFCC) as input. The training database is labelled at the language level. The LID classification is performed at the frame level, using an inference engine to execute the decision tree and classify the firing of the rules. A simple sorting routine is then used to identify the spoken language. Spoken language identification results using the OGI Multi-language Telephone Speech Corpus (OGI-TS) on the 3 language task (English, German and Japanese), are presented.