Abstract: The present disclosure provides acoustic model training methods and systems, and speech synthesis methods and systems. An acoustic model training method may include obtaining a plurality of training samples. Each of the plurality of training samples may include a sample text input, a sample emotion label corresponding to the sample text input, and a sample reference mel spectrum corresponding to the sample text input. The acoustic model training method may include inputting the plurality of training samples into a target model. The target model may include the acoustic model and an auxiliary module. The acoustic model training method may further include iteratively adjusting at least one model parameter of the acoustic model based on a loss target.

Abstract: The present disclosure discloses a method for synthesizing a speech. The method includes generating the speech based on a text with a speech synthesis model, wherein the speech synthesis model includes an embedding layer, a speech synthesis layer, and a position layer; and training the speech synthesis model when an evaluation index meets a preset condition, wherein the evaluation index includes one or more quality indexes determined based on at least a part of the text and at least a part of the speech.

Abstract: The present disclosure discloses a method for synthesizing a speech. The method includes generating the speech based on a text with a speech synthesis model, wherein the speech synthesis model includes an embedding layer, a speech synthesis layer, and a position layer; and training the speech synthesis model when an evaluation index meets a preset condition, wherein the evaluation index includes one or more quality indexes determined based on at least a part of the text and at least a part of the speech.

Abstract: A speech recognition method is provided. The method may include: obtaining speech data and a speech recognition result of the speech data, the speech data including speech of a plurality of speakers, and the speech recognition result including a plurality of words; determining speaking time of each of the plurality of speakers by processing the speech data; determining, based on the speaking times of the plurality of speakers and the speech recognition result, a corresponding relationship between the plurality of words and the plurality of speakers; determining, based on the corresponding relationship, at least one conversion word from the plurality of words, each of the at least one conversion word corresponding to at least two of the plurality of speakers; and re-determining the corresponding relationship between the plurality of words and the plurality of speakers based on the at least one conversion word.

Abstract: The present disclosure discloses a method for synthesizing a speech. The method includes generating the speech based on a text with a speech synthesis model, wherein the speech synthesis model includes an embedding layer, a speech synthesis layer, and a position layer; and training the speech synthesis model when an evaluation index meets a preset condition, wherein the evaluation index includes one or more quality indexes determined based on at least a part of the text and at least a part of the speech.