In this study, we define a new modeling technique called 3D touch-and-drag, wherein users select vertices by simply approaching them with a 3D cursor such as a forefinger. Operations are finished by removing the 3D cursor from a line or plane in 3D space. These lines or planes constrain the modeling operations, as is the case when using 3D widgets. User tests demonstrated that there was no significant difference in movement time between moving a sphere along a line using a pinch gesture and using the proposed technique. Since it is easier to select a vertex by just approaching it, compared with performing a pinch gesture, we believe that 3D touch-and-drag is more efficient than current techniques, while being just as precise.

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.

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.

During the transition period from narrowband to wideband speech transmission services, Artificial Bandwidth Extension (ABE) algorithms are able to reduce the perceptual degradation of narrowband-transmitted speech signals by extending the audio bandwidth. In this paper, we analyze whether the resulting speech quality can be predicted reliably with instrumental models. Estimations from the new ITU standard POLQA, its predecessor WB-PESQ and the diagnostic DIAL model are compared to subjective listener judgments. This comparison reveals that the instrumental measures are not fully able to cope with ABE-processed speech, particularly in predicting ABE rank orders reliably. Reasons for this finding and corresponding diagnoses are discussed. Index Terms: speech quality, artificial bandwidth extension, instrumental quality prediction, speech transmission, diagnosis

Artificial bandwidth extension techniques can be employed in mobile terminals to improve the quality of the far-end speaker's 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.