In the last few years, mobile robots such as floor cleaners, assistive robots, and home telepresence have become an essential part of our day-to-day activities. In human-robot interaction, speech is the preferred way of communication, especially in indoor environments. This paper proposes a speech module to rotate the mobile robot. It has two components, namely, a distant automatic speech recognizer and a sound source localizer. To build distant speech recognizer, far-field speech data is collected at 1, 3, and 5-meters distances. The model performs well even at a 5-meters distance with a Word Error Rate of 40.38% and a Character Error Rate of 28.85%. The direction of arrival of the speech signal is computed from the 4-mic circular array microphone. The speech module is integrated with the Robot Operating System and physically demonstrated on Turtlebot3 Waffle Pi. It is observed that the speech recognizer and sound source localizer work well in the reverberant indoor environment with a small single-board computer.

Accurate pitch estimation in speech signal plays a vital role in several applications. Robust pitch estimation in telephone speech is still a challenge due to the narrow bandwidth of the signal. Electroglottograph (EGG) signal is a reliable means for pitch estimation, however, it’s not practically possible to measure such a signal in many applications. In this work, a method is proposed to synthesize EGG signal from telephone speech using deep feature loss network and subsequently pitch contour is derived from synthesized EGG (SEGG) signal. In order to evaluate the proposed work, CMU-Arctic speech database is used as it contains simultaneous EGG signal recorded. The telephonic speech is derived using International Telecommunications Union ITU-T as specified in the Blizzard Challenge. The robustness of the proposed method is demonstrated under different noisy conditions. The performance of the proposed work is encouraging when compared with other state-of-the-art methods.

The Glottal Closure and Opening instants (GCIs and GOIs) form important events in excitation source signal. These instants represent closing and opening events of vocal folds while producing voiced speech signal. Estimation of such instants from speech signal is beneficial and several applications rely on accurate estimation of Closure and Opening instants. In this work, Electroglottographic like (EGG-like) signal is synthesized from speech signal using Generative Adversarial Network (GAN). The Glottal Closure and Opening instants are located using the derivative of EGG-like signal, which is essentially a difference EGG-like signal. The proposed method is evaluated on CMU-Arctic database, as the database consists of simultaneous recordings of speech and EGG signal, respectively. To evaluate the results, the locations obtained from synthesized EGG-like signal are com-pared with the reference difference EGG signal. The results are evaluated for both seen and unseen conditions. It is shown that the performance of GCI and GOI estimation is comparable to existing state-of-the-art methods.

It is well known that, when noisy speech is transcribed using automatic speech recognition (ASR) systems trained on clean data, a highly degraded recognition performance is obtained. The problemgets further aggravatedwhen the targeted group happens to be child speakers. For children's speech, the acoustic correlates such as pitch and formant frequency vary significantly with age. This makes the recognition of children's speech very challenging. In this paper, we have explored the ways to enhance the noise robustness of ASR systems for children's speech. Towards addressing the same, recently developed front-end acoustic features based on spectral moments (SMAC) are explored. The SMAC features are reported to be more noise robust than the conventional features like the mel-frequency cepsatral coefficients. At the same time, the SMAC features are also noted to be sensitive to the variations in the pitch. To reduce the pitch sensitivity, a spectral smoothing approach based on adaptive-liftering is proposed. Spectral smoothening prior to the computation of spectral moments results in a significant improvement in the robustness to pitch without affecting the noise immunity. To further enhance noise robustness, a foreground speech segmentation and enhancement module is also included in the proposed front-end speech parameterization technique.

In this paper, the speech signal recorded from the desired speaker close to microphone in natural environment is regarded as foreground speech and rest of the interfering sources as background noise . The proposed paper exploits speech production features like glottal closure instants in time domain and vocal tract information in spectral domain to segment the desired speaker's speech and to further enhance it. The foreground speech is perceptually enhanced using the auditory perception feature in mel-frequency domain using mel-cepstral coefficients and its inversion using mel log spectrum approximation filter. The focus is on enhancing the production and perceptual features of foreground speech rather than relying on modeling the interfering sources. The speech data are collected in different natural environments from different speakers in order to evaluate the proposed method. The enhanced speech signals derived at three different stages of the proposed method are evaluated with state-of-the-art methods in terms of subjective and objective measures. The proposed method provides improved performance compared to the considered state-of-the-art methods. In terms of the proposed objective measure foreground to background Ratio, the enhancement approach presented in this paper gives an average improvement of 12 dB as opposed to existing spectral subtraction-based method which provides 3 dB. Moreover, subjective evaluation using 24 different subjects corroborates the objective test results.