论文标题
识别/分割印度区域语言具有奇异价值分解的功能嵌入
Identification/Segmentation of Indian Regional Languages with Singular Value Decomposition based Feature Embedding
论文作者
论文摘要
语言识别（LID）是在给定的口语中识别语言。语言分割同样无关紧要，就像语言标识一样，可以在多语言话语中发现语言边界。在本文中，我们已经在印度区域语言环境中尝试了两种方案，以进行语言识别，因为很少进行工作。两个方案都使用了基于奇异值的特征嵌入。在第一个方案中，将单数值分解（SVD）应用于n-gram语音矩阵，在第二个方案中，SVD应用于差异超级矩阵空间。我们已经观察到，在这两个方案中，55-65％的奇异价值能量足以捕获语言环境。在基于N-Gram的功能表示中，我们已经看到不同的Skipgram模型捕获了不同的语言上下文。我们已经观察到，在短期测试持续时间内，基于监督的特征表示更好，但持续时间较长的测试信号基于N-Gram的功能的执行效果更好。我们还扩展了我们的工作以探索基于语言的细分，我们已经看到了具有十种语言培训模型的四个语言组的细分精度，方案1的表现良好，但具有相同的四种语言培训模型，方案2优于方案1
language identification (LID) is identifing a language in a given spoken utterance. Language segmentation is equally inportant as language identification where language boundaries can be spotted in a multi language utterance. In this paper, we have experimented with two schemes for language identification in Indian regional language context as very few works has been done. Singular value based feature embedding is used for both of the schemes. In first scheme, the singular value decomposition (SVD) is applied to the n-gram utterance matrix and in the second scheme, SVD is applied on the difference supervector matrix space. We have observed that in both the schemes, 55-65% singular value energy is sufficient to capture the language context. In n-gram based feature representation, we have seen that different skipgram models capture different language context. We have observed that for short test duration, supervector based feature representation is better but with a longer duration test signal, n-gram based feature performed better. We have also extended our work to explore language-based segmentation where we have seen that segmentation accuracy of four language group with ten language training model, scheme-1 has performed well but with same four language training model, scheme-2 outperformed scheme-1
