Abstract: An information-seeking dialogue system can be trained using a pipeline process having stages, or components, of passage retrieval (selecting passages relevant to a query from a corpus or knowledge base), re-ranking, and generating a response to the query based on one or more of the re-ranked passages. Each stage, or component, of the pipeline can be individually optimized based on ground truth data.

Abstract: This application relates to a speech synthesis method and apparatus, a model training method and apparatus, and a computer device. The method includes: obtaining to-be-processed linguistic data; encoding the linguistic data, to obtain encoded linguistic data; obtaining an embedded vector for speech feature conversion, the embedded vector being generated according to a residual between synthesized reference speech data and reference speech data that correspond to the same reference linguistic data; and decoding the encoded linguistic data according to the embedded vector, to obtain target synthesized speech data on which the speech feature conversion is performed. The solution provided in this application can prevent quality of a synthesized speech from being affected by a semantic feature in a mel-frequency cepstrum.