Chapter 7 Conclusions and Future researches
7.2. Future researches
To improve the current speech enhancement system, this dissertation proposes two
with the speech recognizer, and the second one is to combine multiple speakers’
location detection approach with the proposed SPFDBB or FDABB.
Currently, the proposed reference-signal-based frequency-domain beamformers are performed in two independent phases: speech purification and then recognition as shown in Figs. 1-5, 3-1 and 4-2. The proposed beamformers designed to reduce the speech distortion and suppress the noise effects assume that improving the quality of the speech waveform will result in better recognition performance and are independent of the recognition system. Although the proposed beamformers can conquer many practical issues, the beamformers still cannot compete with the microphone in a close distance in terms of the ASR rates. Generally, a speech recognizer is a statistical pattern classifier that operates on a sequence of features derived from the waveform. To increase the recognition accuracy in distant-talking environments, the architecture of connecting the proposed beamformers and the speech recognizer as shown in Fig. 7-1 is worth a further study in the further. This architecture enables the beamformer to use the data transmitted from the recognizer and ensures the beamformer enhances those signal components important for ASR. In other words, this architecture enables the designed filters not to undue emphasis on unimportant components.
For example, Seltzer et al. [124-125] proposed a likelihood-maximizing beamformer (LIMABEAM) that integrates the speech recognition system into the filter design process. They proved that incorporating the statistical models of the recognizer into the array processing stage can improve the ASR rates. The goal of the LIMABEAM is not to generate an enhanced output waveform but rather to generate a sequence of features which maximizes the likelihood of generating the correct hypothesis. In other words, the filter coefficient vectors are chosen to maximize the likelihood of the training signal as measured by the recognizer, rather than to improve its SNR or perceptual quality.
Nishiura et al. [128] also proposed a method which combines HMM model and microphone to enhance the speech recognition further.
M M M
Figure 7-1 Speech enhancement system with a combination of beamformer and recognizer
Furthermore, under the same architecture, it is highly possible to combine the proposed speaker’s location detection approach and SPFDBB or FDABB to significantly reduce the possibility of the wrong operation of SPFDBB or FDABB. The wrong operation means that the unmodeled or unexpected speech signal triggers the VAD to switch the beamformers to the speech stage, and thus obtain an error output.
According to the system architecture of frequency-domain beamformers in Chapters 3 and 4, this wrong operation is unavoidable due to the limitation of VAD. However, the proposed multiple speakers’ locations detection approach in Chapter 5, which can detect the unmodeled sound signals, is highly possible to avoid the wrong operation.
Therefore, the unmodeled speech signal could hardly cause the error output under the new system architecture in Fig. 7-2. Based on the system architecture, both the VAD result and speaker’s location detection system are combined to decide the stage of the SPFDBB or FDABB. It means that if the received sound signal is detected as containing speech signal and coming from one of the desired locations, then the system is switched to the speech stage. Consequently, it is also worthwhile to study how to construct the two components with a suitable integration procedure. Figure 7-3 shows
)
Figure 7-2 Overall system structure which integrates the speaker’s location detection approach and SPFDBB or FDABB
Start
Silent stage of the multiple speaker location detection system
Silent stage of SFPDBB or
FDABB Speech stage of the multiple
speaker location detection system
The speech signal comes from one of the
desired location?
Yes
Speech stage of SPFDBB or FDABB
Automatic speech recognizer Silent stage of
SPFDBB or FDABB
No
Figure 7-3 Flowchart of the architecture which integrates the speaker’s location detection approach and SPFDBB or FDABB
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