Jeong-mi Cho

Accent adaptation using Subspace Gaussian Mixture Models

This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be employed for various kinds of acoustic parameter tying. Research results indicate that well-defined sharing of acoustic model parameters in SGMMs can significantly outperform adapted systems based on conventional HMM/GMMs. Furthermore, SGMMs rapidly achieve target acoustic models with small amounts of data. Experiments performed with US and UK English versions of the Wall Street Journal (WSJ) corpora indicate that SGMMs lead to approximately 20% and 8% relative improvements with respect to speaker-independent and speaker-adapted acoustic models respectively over conventional HMM/GMMs. Finally, we demonstrate that SGMMs adapted only with 1.5 hours can reach performance of HMM/GMMs trained with 18 hours.

Integration of sporadic noise model in POMDP-based voice activity detection

Partially observable Markov decision process (POMDP) has recently been applied to a voice activity detector (VAD), which makes it possible to incorporate knowledge about the recording environments in the decision process in order to achieve a more stable performance in noisy situations. In this paper, the model has been further explored to utilize prior knowledge about possible intermittent noise signals such as breath or click sounds, in addition to the stationary background noise types. The experimental result shows that application of sporadic noise models in a POMDP-based VAD reduces the equal error rate of the voice activity decision by about 7% relatively.