# US 10,872,615 B1 · ASR-enhanced speech compression/archiving > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,872,615 B1 - Document ID: US10872615B1 - Status: Granted - Kind: B1 - Application number: 16/371,014 - Filed: 2019-03-31 - Granted: 2020-12-22 - Earliest claimed: 2019-03-31 - Assignees: Medallia, Inc. - Applicants: Voci Technologies, Inc. - Inventors: David Garrod - Web page: https://patentgrep.com/patent/US10872615B1 ## CPC - G10L 15/08: Speech classification or search - G10L 19/04: using predictive techniques - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 40/279: Recognition of textual entities - G10L 15/18: using natural language modelling - H03M 7/60: {General implementation details not specific to a particular type of compression} - G10L 15/14: using statistical models, e.g. Hidden Markov Models [HMMs] (G10L15/18 takes precedence) - G10L 15/183: using context dependencies, e.g. language models - G10L 19/00: Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis (in musical instruments G10H) - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C) ## Abstract A process for compressing an audio speech signal utilizes ASR processing to generate a corresponding text representation and, depending on confidence in the corresponding text representation, selectively applies more, less, or no compression to the audio signal. The result is a compressed audio signal, with corresponding text, that is compact and well suited for searching, analytics, or additional ASR processing. ## Claims ### Claim 1 A computer-implemented process for digitally compressing an audio signal that includes human speech utterances, the process comprising at least the following computer-implemented acts: (1) receiving a digitally encoded audio signal that includes human speech utterances; (2) identifying portions of the digitally encoded audio signal that correspond to the speech utterances; (3) performing automatic speech recognition (ASR) processing to produce, for each utterance, a corresponding (i) original audio representation, (ii) text representation, and (iii) ASR confidence that represents a likelihood that the text representation accurately captures all spoken words contained in the corresponding audio representation; (4) forming a compressed audio signal by concatenating the utterances and, for each utterance, only including either its original audio representation or a compressed version of its original audio representation; (5) storing the compressed audio signal in a non-transitory, computer-readable medium; and (6) wherein, for each utterance, determining whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal comprises determining whether the utterance's corresponding ASR confidence exceeds a predetermined threshold value. ### Claim 2 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are derived from normalized likelihood scores. ### Claim 3 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using an N-best homogeneity analysis. ### Claim 4 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using an acoustic stability analysis. ### Claim 5 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using a word graph hypothesis density analysis. ### Claim 6 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are derived from associated state, phoneme, or word durations. ### Claim 7 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are derived from language model (LM) scores or LM back-off behaviors. ### Claim 8 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using a posterior probability analysis. ### Claim 9 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using a log-likelihood-ratio analysis. ### Claim 10 (depends on 1) A process, as defined in claim 1, wherein the ASR confidence values are computed using a neural net that includes word identity and aggregated words as predictors. ### Claim 11 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether a person speaking the utterance can be identified as belonging to group of known persons-of-interest. ### Claim 12 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether a person speaking the utterance can be identified in any earlier processed audio recording(s). ### Claim 13 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether the utterance is spoken with an excited, angry, or frustrated tone. ### Claim 14 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether words or phrases in the utterance indicate a negative sentiment. ### Claim 15 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether the utterance contains any word(s)/phrase(s) from a preselected list of trigger word(s)/phrase(s). ### Claim 16 (depends on 1) A process, as defined in claim 1, wherein determining, for each utterance, whether to include its original audio representation or a compressed version of its original audio representation in the compressed audio signal additionally comprises determining whether a person speaking the utterance can be identified as a child or an elderly person. ### Claim 17 (depends on 1) A process, as defined in claim 1, wherein performing ASR processing comprises performing an N-best decoding that produces, for each utterance, a corresponding (i) original audio representation, (ii) N alternative text representations, and (iii) N ASR confidence values that represent respective likelihoods that each of the N text representations accurately captures all spoken words contained in the corresponding audio representation. ### Claim 18 (depends on 17) The process of claim 17, wherein N is an integer value of at least two and less than ten.