Patrick Bauer

An HMM-based artificial bandwidth extension evaluated by cross-language training and test

Artificial bandwidth extension techniques can be employed in mobile terminals to improve the quality of the far-end speakers signal at the receiver. To accomplish this, usually statistical models are trained requiring wideband speech material from a language that is expected to be used in the conversation. In practice however, the language of a certain phone conversation is not known to the user equipment. Therefore we investigated the performance of an HMM- based multilingually trained artificial bandwidth extension on speech signals of which the language was unseen in training. The cross-language training and test turned out to cause only minor degradations compared to the use of monolingually trained acoustic models of the language used in test. Our findings indicate that artificial bandwidth extension can be efficiently trained with multilingual speech data without significant losses in speech quality.

HMM-based artificial bandwidth extension supported by neural networks

In telephony applications, artificial bandwidth extension (ABE) can be applied to narrowband (NB) calls for speech quality and intelligibility enhancement. However, high-band extension is challenging due to insufficient mutual information between the lower and upper frequency band in speech. Estimation errors particularly of fricatives /s, z/ are the consequence leading to annoying artifacts, such as lisping. In this paper, two neural networks are employed to support an HMM-based ABE: The first one detects /s, z/ phonemes to assist the estimation process, while the second one corrects the estimated high-band energy. In an absolute category rating test the proposed ABE attains a significantly improved speech quality vs. NB speech. This is confirmed by a comparison category rating test pointing out a speech quality gain of 1.0 CMOS points over NB speech.

ON SPEECH QUALITY ASSESSMENT OF ARTIFICIAL BANDWIDTH EXTENSION

During the transition to wideband speech telephony, artificial bandwidth extension (ABE) could help to preserve customer satisfaction by enhancing speech quality in case of narrowband (NB) calls. However, the assessment of speech quality for ABE systems is still an open question. In the literature, instrumental measures are often used to judge the quality of ABE solutions. When subjective listening tests are considered, they most often use a comparison category rating (CCR) scale and, more rarely, an absolute category rating (ACR) scale. This paper investigates the relevance of instrumental and subjective assessment methods for ABE systems. An ACR and a CCR test are organized. Their results are compared and discussed. Discrepancies between these two tests open the discussion for the design of a proper subjective listening test for ABE systems. Some instrumental measures are also evaluated. A poor correlation between these measures and the subjective results is observed.

On improving speech intelligibility in automotive hands-free systems

Due to its narrow acoustic bandwidth, typical telephone speech is poorly intelligible on a phoneme or syllable basis. Artificial bandwidth extension (ABWE) techniques intend to increase speech intelligibility by estimating and reconstructing higher frequency components up to 7kHz. Hands-free systems in automotive environments could especially benefit from that, since drivers are mentally stressed during telephone conversations. By improving speech intelligibility, their mental load may decrease. Thus driving safety and comfort increase. This paper presents a real-time ABWE implementation integrated into an automotive hands-free system. Its novelty is a phoneme-specific design that yields an improved performance on critical phonemes, especially /s/ and /z/. Hence, typical lisping effects of previous ABWE approaches are reduced. We performed informal listening tests on meaningless vowel-consonant-vowel (VCV) syllables demonstrating a significantly improved fricative intelligibility of ABWE speech vs. NB speech.

Impact of hearing impairment on fricative intelligibility for artificially bandwidth-extended telephone speech in noise

Because of its limited bandwidth, telephone speech is poorly intelligible. Artificial bandwidth extension (ABWE) reconstructs themissing frequencies aiming at, e.g., higher intelligibility. It was recently demonstrated that hearing-impaired persons wearing a hearing aid benefit from ABWE-enhanced telephone speech. However, it is unclear, whether persons without hearing impairment also take profit from ABWE in the same test conditions and if so, to what extent. This paper presents a subjective listening test with normal-hearing subjects based on meaningless German syllables simulating narrowband (NB), ABWE-enhanced and wideband (WB) telephone speech in two noisy listening conditions. The test results reveal a clear impact of hearing impairment on the ABWE capability to improve telephone intelligibility. For a signal-to-noise ratio (SNR) of 0 dB, subjects with and without hearing impairment similarly benefit from ABWE. At 20 dB SNR, hearing-impaired subjects take even more profit in contrast to normal-hearing subjects.