# US 2026/0100196 A1 · Hierarchical speech analysis: a progressive approach to age, gender, and emotion detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100196 A1 - Document ID: US20260100196A1 - Status: Published application - Kind: A1 - Application number: 18/905,436 - Filed: 2024-10-03 - Published: 2026-04-09 - Earliest claimed: 2024-10-03 - Assignees: Tencent America LLC - Applicants: Tencent America LLC - Inventors: Hao ZHANG; Dong YU - Web page: https://patentgrep.com/patent/US20260100196A1 ## CPC - G10L 25/63: for estimating an emotional state - G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] - G06N 3/045: Combinations of networks - G06N 3/09: Supervised learning - G10L 17/18: Artificial neural networks; Connectionist approaches - G10L 25/24: the extracted parameters being the cepstrum - G10L 25/30: using neural networks - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method includes receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker. ## Claims ### Claim 1 A method performed by at least one processor, the method comprising: receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker. ### Claim 2 (depends on 1) The method according to claim 1, wherein the preprocessing the speech signal further comprises: extracting audio features from the speech input signal including at least one of Mel-frequency cepstral coefficients (MFCC), Short-time Fourier Transform coefficients, Wav2Vec embeddings, and text embeddings from an automatic speech recognition system. ### Claim 3 (depends on 1) The method according to claim 1, wherein the preprocessing the speech signal further comprises: performing, on the speech input signal, at least one of normalization, energy equalization, silence removal, and resampling. ### Claim 4 (depends on 1) The method according to claim 1, wherein the detecting, via the first learning stage, the initial age and the initial gender comprises: inputting the preprocessed speech signal into a first neural network architecture to detect the initial age, wherein the first neural network architecture comprises a long short-term memory network (LSTM) followed by a Multi-Head Self-Attention (MHSA) mechanism; and inputting the preprocessed speech signal into a second neural network architecture to detect the initial gender, wherein the second neural network architecture comprises a LSTM followed by a MHSA. ### Claim 5 (depends on 4) The method according to claim 4, further comprising: inputting the initial age, the initial gender, and one or more features extracted from the preprocessed speech signal into a cross-attention mechanism; and inputting an output of the cross-attention mechanism into the second learning stage. ### Claim 6 (depends on 1) The method according to claim 1, wherein the determining, via the second learning stage, the refined age of the speaker, the refined gender of the speaker, and the emotion, further comprises: inputting the initial age and the initial gender into a Conditional Layer Normalization CLN)Transformer module. ### Claim 7 (depends on 6) The method according to claim 6, wherein the CLN transformer module comprises: a Multi-Head Self-Attention (MHSA) mechanism that receives the initial age, the initial gender, and position encoding information indicating a sequence order of data input into the MHSA; a first Add and CLN layer that receives an output of the MHSA; a feed-forward network (FFN) that receives and output of the first Add and CLN layer, wherein the FFN comprises two linear transformations separated by a rectified linear unit (ReLU); and a second Add and CLN layer that receives an output of the FFN. ### Claim 8 An apparatus comprising: at least one memory configured to store program code; and at least one processor configured to read the program code and operate as instructed by the program code, the program code including: receiving coded configured to cause the at least one processor to receive a speech input signal of a speaker; preprocessing code configured to cause the at least one processor to preprocess the speech signal to generate a preprocessed speech signal; detecting code configured to cause the at least one processor to detect, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining code configured to cause the at least one processor to determine, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker. ### Claim 9 (depends on 8) The apparatus according to claim 8, wherein the preprocessing code further comprises: extracting code configured to cause the at least one processor to extract audio features from the speech input signal including at least one of Mel-frequency cepstral coefficients (MFCC), Short-time Fourier Transform coefficients, Wav2Vec embeddings, and text embeddings from an automatic speech recognition system. ### Claim 10 (depends on 8) The apparatus according to claim 8, wherein the preprocessing code further comprises: performing code configured to cause the at least one processor to perform, on the speech input signal, at least one of normalization, energy equalization, silence removal, and resampling. ### Claim 11 (depends on 8) The apparatus according to claim 8, wherein the detecting code further comprises: first neural network code configured to cause the at least one processor to input the preprocessed speech signal into a first neural network architecture to detect the initial age, wherein the first neural network architecture comprises a long short-term memory network (LSTM) followed by a Multi-Head Self-Attention (MHSA) mechanism; and second neural network code configured to cause the at least one processor to input the preprocessed speech signal into a second neural network architecture to detect the initial gender, wherein the second neural network architecture comprises a LSTM followed by a MHSA. ### Claim 12 (depends on 11) The apparatus according to claim 11, wherein the program code further comprises: cross-attention code configured to cause the at least one processor to input the initial age, the initial gender, and one or more features extracted from the preprocessed speech signal into a cross-attention mechanism, wherein the determining code is further configured to cause the at least one processor to input an output of the cross-attention mechanism into the second learning stage. ### Claim 13 (depends on 8) The apparatus according to claim 8, wherein the determining code further comprises: Conditional Layer Normalization (CLN) code configured to cause the at least one processor to input the initial age and the initial gender into a Conditional Layer Normalization CLN) Transformer module. ### Claim 14 (depends on 13) The apparatus according to claim 13, wherein the CLN transformer module comprises: a Multi-Head Self-Attention (MHSA) mechanism that receives the initial age, the initial gender, and position encoding information indicating a sequence order of data input into the MHSA; a first Add and CLN layer that receives an output of the MHSA; a feed-forward network (FFN) that receives and output of the first Add and CLN layer, wherein the FFN comprises two linear transformations separated by a rectified linear unit (ReLU); and a second Add and CLN layer that receives an output of the FFN. ### Claim 15 A non-transitory computer readable medium having instructions stored therein, which method performed by at least one processor, the method comprising: receiving a speech input signal of a speaker; preprocessing the speech signal to generate a preprocessed speech signal; detecting, via a first learning stage that receives the preprocessed speech signal, an initial age and an initial gender of the speaker; and determining, via a second learning stage that receives the initial age and the initial gender as inputs, a refined age of the speaker, a refined gender of the speaker, and an emotion of the speaker. ### Claim 16 (depends on 15) The non-transitory computer readable medium according to claim 15, wherein the preprocessing the speech signal further comprises: extracting audio features from the speech input signal including at least one of Mel-frequency cepstral coefficients (MFCC), Short-time Fourier Transform coefficients, Wav2Vec embeddings, and text embeddings from an automatic speech recognition system. ### Claim 17 (depends on 15) The non-transitory computer readable medium according to claim 15, wherein the preprocessing the speech signal further comprises: performing, on the speech input signal, at least one of normalization, energy equalization, silence removal, and resampling. ### Claim 18 (depends on 15) The non-transitory computer readable medium according to claim 15, wherein the detecting, via the first learning stage, the initial age and the initial gender comprises: inputting the preprocessed speech signal into a first neural network architecture to detect the initial age, wherein the first neural network architecture comprises a long short-term memory network (LSTM) followed by a Multi-Head Self-Attention (MHSA) mechanism; and inputting the preprocessed speech signal into a second neural network architecture to detect the initial gender, wherein the second neural network architecture comprises a LSTM followed by a MHSA. ### Claim 19 (depends on 18) The non-transitory computer readable medium according to claim 18, further comprising: inputting the initial age, the initial gender, and one or more features extracted from the preprocessed speech signal into a cross-attention mechanism; and inputting an output of the cross-attention mechanism into the second learning stage. ### Claim 20 (depends on 15) The non-transitory computer readable medium according to claim 15, wherein the determining, via the second learning stage, the refined age of the speaker, the refined gender of the speaker, and the emotion, further comprises: inputting the initial age and the initial gender into a Conditional Layer Normalization CLN)Transformer module.