A response sentence generation apparatus comprising: conversion means for converting an input voice of a user into text information; extraction means for extracting prosodic information from the input voice; specifying means for specifying an emotion occurrence word indicating an occurrence of an emotion of the user based on the text information and the prosodic information; generation means for selecting a character string including the specified emotion occurrence word from the text information and generating a response sentence by performing predetermined processing on the selected character string; and dictionary information in which a candidate for the emotion occurrence word is registered in advance, wherein the extraction means is adapted to extract an emphasized voice in the input voice as the prosodic information by performing a fundamental-frequency analysis on the input voice, the specifying means is adapted to specify a character string corresponding to the emphasized voice indicated by the prosodic information as the emotion occurrence word from the text information, the conversion means is adapted to associate information on a time at which each sound in the input voice occurs with a respective character in the text information, the extraction means is adapted to include information on a time at which each sound in the emphasized voice has occurred in the prosodic information and extracts it together with the prosodic information, the specifying means is adapted to specify a character string corresponding to the information on the time included in the prosodic information as the emotion occurrence word from the text information, and the specifying means is adapted to specify the emotion occurrence word by further taking the dictionary information into consideration, so that, in a case where a plurality of parts are indicated as emphasized sounds in the prosodic information, it is possible to narrow down the parts corresponding to the emphasized sounds based on candidates for emotion occurrence words registered in the dictionary information. conversion means for converting an input voice of a user into text information; extraction means for extracting prosodic information from the input voice; specifying means for specifying an emotion occurrence word indicating an occurrence of an emotion of the user based on the text information and the prosodic information; generation means for selecting a character string including the specified emotion occurrence word from the text information and generating a response sentence by performing predetermined processing on the selected character string; and dictionary information in which a candidate for the emotion occurrence word is registered in advance, wherein the extraction means is adapted to extract an emphasized voice in the input voice as the prosodic information by performing a fundamental-frequency analysis on the input voice, the specifying means is adapted to specify a character string corresponding to the emphasized voice indicated by the prosodic information as the emotion occurrence word from the text information, the conversion means is adapted to associate information on a time at which each sound in the input voice occurs with a respective character in the text information, the extraction means is adapted to include information on a time at which each sound in the emphasized voice has occurred in the prosodic information and extracts it together with the prosodic information, the specifying means is adapted to specify a character string corresponding to the information on the time included in the prosodic information as the emotion occurrence word from the text information, and the specifying means is adapted to specify the emotion occurrence word by further taking the dictionary information into consideration, so that, in a case where a plurality of parts are indicated as emphasized sounds in the prosodic information, it is possible to narrow down the parts corresponding to the emphasized sounds based on candidates for emotion occurrence words registered in the dictionary information.