利用运动状态增强深度神经网络的瓶颈特征用于人形机器人的语音识别
Augmenting Bottleneck Features of Deep Neural Network Employing Motor State for Speech Recognition at Humanoid Robots
摘要：
对于人形机器人，机器人在运动或晃动时，由于电机、风扇等机械部件产生的内部噪声严重影响了语音识别的准确性。提出了一种新的人形机器人语音识别系统，该系统利用电机的开/关状态作为辅助信息来寻找相关的输入特征。为此，我们考虑瓶颈特征，这些特征已成功应用于基于深度神经网络（DNN）的自动语音识别（ASR）系统。当学习要捕捉的瓶颈特征时，除了声学特征作为第一深度神经网络（DNN）的输入之外，我们首先利用电机开/关状态数据作为补充信息，用于初步的声学建模。然后，用于主要声学建模的第二DNN采用从第一DNN抛出的瓶颈特征和声学特征两者。在TIMIT数据库上进行音素出错率（PER）测试，实验结果表明，与传统系统相比，该算法有明显的提高（相对提高11%）。
英文摘要：
As for the humanoid robots, the internal noise, which is generated by motors, fans and mechanical components when the robot is moving or shaking its body, severely degrades the performance of the speech recognition accuracy. In this paper, a novel speech recognition system robust to ego-noise for humanoid robots is proposed, in which on/off state of the motor is employed as auxiliary information for finding the relevant input features. For this, we consider the bottleneck features, which have been successfully applied to deep neural network (DNN) based automatic speech recognition (ASR) system. When learning the bottleneck features to catch, we first exploit the motor on/off state data as supplementary information in addition to the acoustic features as the input of the first deep neural network (DNN) for preliminary acoustic modeling. Then, the second DNN for primary acoustic modeling employs both the bottleneck features tossed from the first DNN and the acoustics features. When the proposed method is evaluated in terms of phoneme error rate (PER) on TIMIT database, the experimental results show that achieve obvious improvement (11% relative) is achieved by our algorithm over the conventional systems.
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