CAJ | 학술논문

为获得较为鲁棒的识别性能，一般的语音识别系统中都会在后端加入一个置信度判决模块，以实现识别错误检测和集外词拒识等功能。针对命令词语音识别系统，传统的基于Filler模型的置信度方法由于自身模型结构的限制，性能相对有限，尤其是对集外词的检测效果不好。为此，使用了一种基于音节循环的置信度判决方法，并对该方法的解码网络进行精简，以满足实用化的效率要求。在中文命令词测试集上的实验结果表明，该方法相对于基于Filler模型的置信度方法对识别效果与识别效率都有了较大的提升。
위획득교위로봉적식별성능，일반적어음식별계통중도회재후단가입일개치신도판결모괴，이실현식별착오검측화집외사거식등공능。침대명령사어음식별계통，전통적기우Filler모형적치신도방법유우자신모형결구적한제，성능상대유한，우기시대집외사적검측효과불호。위차，사용료일충기우음절순배적치신도판결방법，병대해방법적해마망락진행정간，이만족실용화적효솔요구。재중문명령사측시집상적실험결과표명，해방법상대우기우Filler모형적치신도방법대식별효과여식별효솔도유료교대적제승。
To improve robustness of automatic speech recognition system, a post-processing module called Confidence Measure (CM)is usually employed in state-of-the-art system to serve as error detection, Out-Of-Vocabulary(OOV)rejection, etc. In a command word speech recognition system, a traditional filler-based CM is adopted, the performance of which is poor especially for OOV detection. This paper presents an improved syllable loop based confidence measure method which simplifies the decod-ing network to meet the real application requirements. Experimental results on a Mandarin command word recognition show that the proposed method achieves significant improvement in both performance and efficiency when comparing to the baseline.