Subjective and objective quality assessment of noise reduced speech signals

Abstract

We propose a spatial subtraction array (SSA) and known noise superimposition to achieve a robust hands-free speech recognition under noisy environments. In the proposed SSA, noise reduction is achieved by subtracting the estimated noise power spectrum from the target speech power spectrum to be enhanced in the mel-scale filter bank domain. This offers a realization of error-robust spatial spectral subtraction with few computational complexities. In addition, we introduce known noise superimposition technique in the mel-scale filter bank domain, and utilize the matched acoustic model for the known noise. This can compensate the acoustic model mismatch and mask the residual noise component in SSA. The experimental results obtained under a real environment reveal that word accuracy of the proposed method is greater than that of the conventional method even when the target user moves between -10 and +10 degrees around the microphone array. Recently, hands-free speech communication is becoming increasingly necessary for teleconferences, in-car phones, and PC-based IP telephony. Since speech signals acquired by a distant microphone are generally corrupted by ambient noise, noise reduction is indispensable for ensuring speech quality. To investigate the effect of noise reduction on speech quality, the subjective quality assessment of noisy speech signab and noise reduced speech signals was performed. This result confirmed that the difference of noise types gives little effect on the subjective quality and the subjective quality is more sensitive to the speech distortion than the residual noise. This paper also investigated the applicability of the PEsQ to the objective quality assessment o f noise reduction algorithms. The experimental result showed that the objective quality correlates well with the subjective quality. However, there was the case that the objective quality is lower than the subjective quality in the specific noise reduction algorithms. 28

Non-reference Objective Quality Evaluation for Noise-Reduced Speech Using Overall Quality Estimation Model

“…P.862 [1], is the most widelyused of this type. However, the PESQ method can give poor estimates for noise-reduced speech, as we reported in [2]. Egi et al recently proposed a full-reference objective quality evaluation method for noise-reduced speech and showed its effectiveness [3].…”

TCD-VoIP, a research database of degraded speech for assessing quality in VoIP applications

“…Falk and Chan [15] made use of car noise, Hoth noise and babble noise, while comparing PESQ to two other objective quality models. Kitawaki and Yamada [16] used subway, car, babble and exhibition noise while comparing PESQ with subjective results for noise-reduced speech. Hu and Loizou [13] used 8 types of noise in developing the NOIZEUS corpus of noisy speech data for evaluating speech enhancement algorithms.…”

Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations

“…The study indicates that, for this particular scenario, ensuring enough resources to active participants and prioritizing the audio quality are key to the overall audio-visual quality [28]. For audio content, a range of studies has focused on investigating the perceived quality effect of acoustic background noise [34,35]. In the study presented by Wendt et al [29], speech intelligibility was compared across different levels of background noise and syntactic structure complexity.…”