Tomohiro Narita

Advanced ASR Technologies for Mitsubishi Electric Speech Applications.

Mitsubishi Electric Corporation has been developing speech applications for 20 years. Our main targets are car navigation systems, elevator-controlling systems, and other industrial devices. This chapter deals with automatic speech recognition technologies which were developed for these applications. To realize real-time processing with small resources, syllable N-gram-based text search is proposed. To deal with reverberant environments in elevators, spectral-subtraction-based dereverberation techniques with reverberation time estimation are used. In addition, discriminative methods for acoustic and language models are developed.

Optimal automatic speech recognition system selection for noisy environments

To improve the performance of noisy automatic speech recognition (ASR), it is effective to prepare multiple ASR systems that can address the large varieties of noise. However, the optimal ASR system is different for each environment and mismatches between training and testing degrade ASR performance. In this situation, the overall system combination of multiple systems is effective; however, the computational resources increase in proportion to the number of systems. This paper proposes a method to select an optimal single system from multiple systems. The selection is based on the estimated word error rates of a respective system by using the i-vector similarities between training and test data. The experiments on the third CHiME challenge show that our proposed method can efficiently select a single system from multiple systems with different speech enhancement and feature transformation methods to improve the overall performance without increasing computational resources.

Multi-channel non-negative matrix factorization with binary mask initialization for automatic speech recognition

Non-negative Matrix Factorization (NMF) factorizes a non-negative matrix into two non-negative matrices. In the field of acoustics, multichannel expansion has been proposed to consider spatial information for sound source separation. Conventional multi-channel NMF has a difficulty in an initial-value dependency of the separation performance due to local minima. This paper proposes initial value settings by using binary masking based sound source separation whose masks on the time frequency domain are calculated from the time difference of arrival of each source. The proposed method calculates initial spatial correlation matrices using separated sources by binary masking. The music separation experiments confirmed that the separation performance of the proposed method was better than that of the conventional method. In addition, we evaluated initial value settings by using binary masking for automatic speech recognition (ASR) tasks in noisy environments. The ASR experiments confirmed that appropriate initi...