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==== Zang, X., Weng, F., & Zang, X. (2022). Foreign Accent Conversion using Concentrated Attention. In 2022 IEEE International Conference on Knowledge Graph (ICKG). Retrieved from <nowiki>https://ieeexplore.ieee.org/document/978-1-6654-5101-7</nowiki> ==== '''Summary:''' This paper introduces a novel method for foreign accent conversion (FAC) utilizing Phonetic Posteriorgrams (PPGs) and Log-scale Fundamental frequency (Log-FO) to address phonetic and prosody mismatches. The proposed approach employs concentrated attention to enhance the alignment of input sequences and mel-spectrograms, selecting the top k highest score values in the attention matrix row by row. The method is evaluated through objective metrics and demonstrates improved voice naturalness, speaker similarity, and accent similarity. '''RQ:''' How can foreign accent conversion be improved to achieve better alignment and naturalness in synthesized speech while preserving the source speaker's identity? '''Hypothesis:''' Implementing concentrated attention in the foreign accent conversion process will result in more accurate alignment of input sequences with mel-spectrograms, leading to improved accent conversion quality and naturalness in synthesized speech. '''Conclusion:''' The proposed method using concentrated attention for foreign accent conversion delivers comparable or better results than previous methods in terms of voice naturalness and accent similarity. The concentrated attention mechanism effectively focuses on the most relevant frames for better alignment and synthesized speech quality. '''Critical observations:''' The concentrated attention mechanism is found to be beneficial for achieving better alignment between input sequences and target sequences, resulting in improved speech synthesis. '''Relevance:''' The research is relevant to the field of speech synthesis and voice conversion, particularly for applications that require the alteration of accents while maintaining the original speaker's voice characteristics. This work contributes to the development of systems that can aid in language learning, dubbing, and other scenarios where accent modification is beneficial, enhancing the quality and naturalness of synthesized speech.
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