US 12,437,748 B2
Spoken language processing method and apparatus, and storage medium
Yinhui Zhang, Beijing (CN)
Assigned to BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD., Beijing (CN)
Filed by Beijing Baidu Netcom Science Technology Co., Ltd., Beijing (CN)
Filed on Oct. 14, 2022, as Appl. No. 17/965,869.
Claims priority of application No. 202210323762.X (CN), filed on Mar. 29, 2022.
Prior Publication US 2023/0317058 A1, Oct. 5, 2023
Int. Cl. G10L 15/02 (2006.01); G06F 17/16 (2006.01); G06F 18/2415 (2023.01); G06F 18/25 (2023.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G10L 15/183 (2013.01)
CPC G10L 15/02 (2013.01) [G06F 17/16 (2013.01); G06F 18/2415 (2023.01); G06F 18/253 (2023.01); G06F 40/166 (2020.01); G06F 40/20 (2020.01); G06F 40/279 (2020.01); G06F 40/30 (2020.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G10L 15/183 (2013.01)] 9 Claims
OG exemplary drawing
 
1. A spoken language processing method, comprising:
obtaining spoken text information by collecting speech information and recognizing the speech information through automatic speech recognition;
determining a word feature of a word in the spoken text information;
determining a correlation feature of the word in the spoken text information;
determining an effect of the word on fluency of the spoken text information according to the word feature of the word and the correlation feature of the word; and
correcting the spoken text information according to the effect of the word on the fluency of the spoken text information to obtain target text information so as to improve quality of the spoken text information;
wherein determining the correlation feature of the word in the spoken text information comprises:
determining a word vector matrix of the word in the spoken text information; and
determining the correlation feature of the word according to the word vector matrix of the word; and
wherein determining the correlation feature of the word according to the word vector matrix of the word comprises:
determining a Hadamard product of a word vector matrix of an i-th word and a word vector matrix of a i-th word in the spoken text information;
determining a correlation matrix according to the Hadamard product; and
performing three-dimensional (3D) feature extraction on the correlation matrix to obtain correlation features of the i-th word and the j-th word, and
wherein i and j are natural numbers.