Speech Recognition In Real Environments Research Articles

Recommendation Effects of a Social Robot for Advertisement-Use Context in a Shopping Mall

We developed a coupon-giving robot system for a shopping mall to explore possible applications using social robots in daily environments, particularly for advertising. The system provided information through conversations with people. The robot was semi-autonomous, which means that it was partly controlled by a human operator, to cope with the difficulty of speech recognition in real environments. We conducted two field trials to investigate two kinds of effectiveness related to recommendations: the presence of a robot and different conversation schemas. Although a robot can strongly attract people with its presence and interaction, it remains unknown whether it can increase the effects of advertisements in real environments. Our field trial results show that a small robot increased the number of people who printed coupons more than a normal-sized robot. The number of people who printed coupons also increased when the robot asked visitors to freely select from all coupon candidates or to listen to its recommendation.

Noise Robust Voice Activity Detection Based on Switching Kalman Filter

This paper addresses the problem of voice activity detection (VAD) in noisy environments. The VAD method proposed in this paper is based on a statistical model approach, and estimates statistical models sequentially without a priori knowledge of noise. Namely, the proposed method constructs a clean speech / silence state transition model beforehand, and sequentially adapts the model to the noisy environment by using a switching Kalman filter when a signal is observed. In this paper, we carried out two evaluations. In the first, we observed that the proposed method significantly outperforms conventional methods as regards voice activity detection accuracy in simulated noise environments. Second, we evaluated the proposed method on a VAD evaluation framework, CENSREC-1-C. The evaluation results revealed that the proposed method significantly outperforms the baseline results of CENSREC-1-C as regards VAD accuracy in real environments. In addition, we confirmed that the proposed method helps to improve the accuracy of concatenated speech recognition in real environments.

Hands-free speech recognition in real environments using microphone array and 2-levels MLLR adaptation as a front-end system for conversational TV

Hands-free speech recognition in real environments using microphone array and 2-levels MLLR adaptation as a front-end system for conversational TV

Distant-talking speech recognition based on a 3-D Viterbi search using a microphone array

This paper focuses on microphone arrays to realize distant-talking speech recognition in real environments. In distant-talking situations, users can speak at arbitrary positions while moving. Therefore, it,is very important for high quality speech acquisition using microphone arrays to localize a talker accurately. However, it is very difficult to localize a moving talker in noisy and reverberant environments. The talker localization errors result in performance degradation of speech recognition. One way to solve this problem is to integrate the speech recognition process and the talker localization into a unified framework. This paper proposes a new speech recognition algorithm based on a three-dimensional (3-D) Viterbi search. The 3-D Viterbi method extracts a direction-time sequence of parameter vectors by steering a beam to every direction in every frame, then finds the most likely path in a 3-D trellis space composed of talker directions, input frames and HMM states. This means that speech recognition and talker localization are performed simultaneously within a statistical framework. To evaluate the performance of the 3-D Viterbi method, recognition experiments for real environment data were carried out. The results confirmed that the 3-D Viterbi method drastically improves the recognition performance for the moving talker case as well as for the fixed-position talker case.