The accuracy of automatic speech recognition in a car is significantly degraded in a very low SNR (Signal to Noise Ratio) situation such as "Fan high" or "Window open". In such cases, speech signals are often buried in broadband noise. Although several existing noise reduction algorithms are known to improve the accuracy, other approaches that can work with them are still required for further improvement. One of the candidates is enhancement of the harmonic structures in human voices. However, most conventional approaches are based on comb filtering, and it is difficult to use them in practical situations, because their assumptions for F0 detection and for voiced/unvoiced detection are not accurate enough in realistic noisy environments. In this paper, we propose a new approach that does not rely on such detection. An observed power spectrum is directly converted into a filter for speech enhancement, by retaining only the local peaks considered to be harmonic structures in the human voice. In our experiments, this approach reduced the word error rate by 17% in realistic automobile environments. Also, it showed further improvement when used with existing noise reduction methods.

Recently, automatic speech recognition in a car has practical uses for applications like car-navigation and hands-free telephone dialers. For noise robustness, the current successes are based on the assumption that there is only a stationary cruising noise. Therefore, the recognition rate is greatly reduced when there is music or news coming from a radio or a CD player in the car. Since reference signals are available from such in-vehicle units, there is great hope that echo cancellers can eliminate the echo component in the observed noisy signals. However, previous research reported that the performance of an echo canceller is degraded in very noisy conditions. This implies it is desirable to combine the processes of echo cancellation and noise reduction. In this paper, we propose a system that uses echo cancellation and spectral subtraction simultaneously. A stationary noise component for spectral subtraction is estimated through the adaptation of an echo canceller. In our experiments, this system significantly reduced the errors in automatic speech recognition compared with the conventional combination of echo cancellation and spectral subtraction.

It is believed that distant-talking speech recognition in a noisy environment requires a large-scale microphone array. However, this cannot fit into small consumer devices. Our objective is to improve the performance with a limited number of microphones (preferably only left and right). In this paper, we focused on a profile that is the shape of the power distribution according to the beamforming direction. An observed profile can be decomposed into known profiles for directional sound sources and a non-directional background sound source. Evaluations confirmed this method reduced the CER (Character Error Ratio) for the dictation task by more than 20% compared to a conventional 2-channel Adaptive Spectral Subtraction beamformer in a non-reverberant environment.

In a two-microphone approach, interchannel differences in time (ICTD) and interchannel differences in sound level (ICLD) have generally been used for sound source localization. But those cues are not effective for vertical localization in the median plane (direct front). For that purpose, spectral cues based on features of head-related transfer functions (HRTF) have been investigated, but they are not robust enough against signal variations and environmental noise. In this paper, we use a "profile" as a cue while using a combination of reflectors specially designed for vertical localization. The observed sound is converted into a profile containing information about reflections as well as ICTD and ICLD data. The observed profile is decomposed into signal and noise by using template profiles associated with sound source locations. The template minimizing the residual of the decomposition gives the estimated sound source location. Experiments show this method can correctly provide a rough estimate of the vertical location even in a noisy environment.