“针对 Transformer 模型在蒙古语语音识别任务中无法学习到带有控制符的蒙古语词和语音之间的对应关系,造成模型对蒙古语的不适应问题。提出一种面向 Transformer 模型的蒙古语词编码方法,方法使用蒙古语字母特征与词特征进行混合编码,通过结合蒙古语字母信息使 Transformer 模型能够区分带有控制符的蒙古语词,学习到蒙古语词与语音之间的对应关系。在 IMUT-MC 数据集上,构建 Transformer 模型并进行了词特征编码方法的消融实验和对比实验。消融实验结果表明,词特征编码方法在 HWER、WER、SER 上分别降低了 23.4%、6.9%、2.6%;对比实验结果表明,词特征编码方法领先于所有方法,HWER 和 WER 分别达到 11.8%、19.8%。”

“说话人特征提取模型提取到的说话人特征之间区分性低,使得蒙古语声学模型无法学习到区分性信息,导致模型无法适应不同说话人。提出一种基于注意力的说话人自适应方法,方法引入神经图灵机进行自适应,增加记忆模块存放说话人特征,采用注意力机制计算记忆模块中说话人特征与当前语音说话人特征的相似权重矩阵,通过权重矩阵重新组合成说话人特征s-vector,进而提高说话人特征之间的区分性。在IMUT-MCT数据集上,进行说话人特征提取方法的消融实验、模型自适应实验和案例分析。实验结果表明,对比不同说话人特征s-vector、i-vector与d-vector,s-vector比其他两种方法的SER和WER分别降低4.96%、1.08%;在不同的蒙古语声学模型上进行比较,提出的方法相对于基线均有性能提升。”

The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform’s activity. In human music comments, there exists high distinction and diverse perspectives for the same song. In other words, for a song, different comments stem from different musical perspectives. However, to date, this characteristic has not been considered well in research on automatic comment generation. The existing methods tend to generate common and meaningless comments. In this paper, we propose an effective multi-perspective strategy to enhance the diversity of the generated comments. The experiment results on two music comment datasets show that our proposed model can effectively generate a series of diverse music comments based on different perspectives, which outperforms state-of-the-art baselines by a substantial margin.

Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics. However, to date, it has not been considered in research on automatic poetry generation. In this paper, we propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation. Our model relies on a continuous latent variable as a rhetoric controller to capture various rhetorical patterns in an encoder, and then incorporates rhetoric-based mixtures while generating modern Chinese poetry. For metaphor and personification, an automated evaluation shows that our model outperforms state-of-the-art baselines by a substantial margin, while human evaluation shows that our model generates better poems than baseline methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics.