A speech recognition error correction method comprising: acquiring to-be-recognized speech data and a first recognition result of the speech data; extracting a keyword from the first recognition result; re-recognizing the speech data with reference to context information in the first recognition result and the keyword to obtain a second recognition result; and determining a final recognition result based on the second recognition result, characterized in that: the keyword is a field-specific word, and the re-recognizing the speech data with reference to context information in the first recognition result and the keyword to obtain a second recognition result comprises: acquiring an acoustic feature of the speech data; and inputting the acoustic feature of the speech data, the first recognition result and the keyword to a pre-trained speech recognition error correction model to obtain the second recognition result, wherein the speech recognition error correction model is obtained by training a preset model with an error-correction training data set, wherein the error-correction training data set comprises at least one group of error-correction training data, and each group of error-correction training data comprises an acoustic feature of a piece of speech data, a text corresponding to the piece of speech data, a first recognition result corresponding to the piece of speech data, and a keyword in the first recognition result. acquiring to-be-recognized speech data and a first recognition result of the speech data; extracting a keyword from the first recognition result; re-recognizing the speech data with reference to context information in the first recognition result and the keyword to obtain a second recognition result; and determining a final recognition result based on the second recognition result, characterized in that: the keyword is a field-specific word, and the re-recognizing the speech data with reference to context information in the first recognition result and the keyword to obtain a second recognition result comprises: acquiring an acoustic feature of the speech data; and inputting the acoustic feature of the speech data, the first recognition result and the keyword to a pre-trained speech recognition error correction model to obtain the second recognition result, wherein the speech recognition error correction model is obtained by training a preset model with an error-correction training data set, wherein the error-correction training data set comprises at least one group of error-correction training data, and each group of error-correction training data comprises an acoustic feature of a piece of speech data, a text corresponding to the piece of speech data, a first recognition result corresponding to the piece of speech data, and a keyword in the first recognition result. the keyword is a field-specific word, and the re-recognizing the speech data with reference to context information in the first recognition result and the keyword to obtain a second recognition result comprises: acquiring an acoustic feature of the speech data; and inputting the acoustic feature of the speech data, the first recognition result and the keyword to a pre-trained speech recognition error correction model to obtain the second recognition result, wherein the speech recognition error correction model is obtained by training a preset model with an error-correction training data set, wherein the error-correction training data set comprises at least one group of error-correction training data, and each group of error-correction training data comprises an acoustic feature of a piece of speech data, a text corresponding to the piece of speech data, a first recognition result corresponding to the piece of speech data, and a keyword in the first recognition result. acquiring an acoustic feature of the speech data; and inputting the acoustic feature of the speech data, the first recognition result and the keyword to a pre-trained speech recognition error correction model to obtain the second recognition result, wherein the speech recognition error correction model is obtained by training a preset model with an error-correction training data set, wherein the error-correction training data set comprises at least one group of error-correction training data, and each group of error-correction training data comprises an acoustic feature of a piece of speech data, a text corresponding to the piece of speech data, a first recognition result corresponding to the piece of speech data, and a keyword in the first recognition result.