RECOGNITION OF HIGHLY IMBALANCED CODE-MIXED BILINGUAL SPEECH WITH FRAME-LEVEL LANGUAGE DETECTION BASED ON BLURRED POSTERIORGRAM

In this work, we proposed a new framework for recognition of highly imbalanced code-mixed bilingual speech using an additional frame-level language detector in the conventional recognition system. Blurred posteriorgram features (BPFs) are also proposed to be used in the language detector. The approach was evaluated with real spontaneous lectures offered at National Taiwan University. The highly imbalanced language distribution in code-mixed speech makes the task difficult. Preliminary experimental results showed not only very good performance improvement, but the improvement is complementary to that brought by better acoustic models, whether due to better adaptation approach or increased training data. The code-mixed bilingual speech is frequently used in the daily lives of many people in the globalized world today.