Methods and Systems for Performing Synchronization of Audio with Corresponding Textual Transcriptions and Determining Confidence Values of the Synchronization

• US 20110288862A1
• Filed: 05/18/2010
• Published: 11/24/2011
• Est. Priority Date: 05/18/2010
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Shazam Entertainment Limited (Apple Inc.)
• Inventors: Todic, Ognjen
• Granted Patent: US 8,543,395 B2
• Time in Patent Office: Days
• US Class Current: 704/235
• CPC Class Codes: G10H 1/0008 Associated control or indicating means
• CPC Class Codes: G10H 1/368 displaying animated or moving pictures synchronized with the music or audio part
• CPC Class Codes: G10H 2210/046 for differentiation between music and non-music signals, based on the identification of musical parameters, e.g. based on tempo detection
• CPC Class Codes: G10H 2210/091 for performance evaluation, i.e. judging, grading or scoring the musical qualities or faithfulness of a performance, e.g. with respect to pitch, tempo or other timings of a reference performance
• CPC Class Codes: G10H 2220/011 Lyrics displays, e.g. for karaoke applications
• CPC Class Codes: G10H 2250/021 Dynamic programming, e.g. Viterbi, for finding the most likely or most desirable sequence in music analysis, processing or composition
• CPC Class Codes: G10L 15/05 Word boundary detection

Abstract

Methods and systems for performing audio synchronization with corresponding textual transcription and determining confidence values of the timing-synchronization are provided. Audio and a corresponding text (e.g., transcript) may be synchronized in a forward and reverse direction using speech recognition to output a time-annotated audio-lyrics synchronized data. Metrics can be computed to quantify and/or qualify a confidence of the synchronization. Based on the metrics, example embodiments describe methods for enhancing an automated synchronization process to possibly adapted Hidden Markov Models (HMMs) to the synchronized audio for use during the speech recognition. Other examples describe methods for selecting an appropriate HMM for use.

Claims

1. A method of processing audio signals, comprising:

receiving an audio signal comprising vocal elements;

a processor performing an alignment of the vocal elements with corresponding textual transcriptions of the vocal elements;

based on the alignment, determining timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements; and

outputting a confidence metric indicating a level of certainty for the timing boundary information for the duration of the portion of the vocal elements.

2. The method of claim 1, wherein performing the alignment of the vocal elements with corresponding textual transcriptions of the vocal elements comprises performing a forward alignment of the vocal elements processed in a forward direction with corresponding textual transcriptions of the vocal elements, wherein determining timing boundary information comprises determining forward timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements processed in the forward direction, and wherein the method further comprises:

performing a reverse alignment of the vocal elements processed in a reverse direction with corresponding reverse textual transcriptions of the vocal elements;

determining reverse timing boundary information associated with an elapsed amount of time for a duration of the portion of the vocal elements processed in the reverse direction; and

based on a comparison between the forward timing information and the reverse timing information, outputting the confidence metric indicating a level of certainty for the forward timing boundary information.

3. The method of claim 2, further comprising:

determining a difference between the forward timing boundary information and the reverse timing boundary information;

making a comparison of the difference to a predefined threshold; and

based on the comparison, marking the portion of the vocal elements with a confidence level.

4. The method of claim 2, wherein the audio signal is a musical song, and wherein the portion of the vocal elements is a line of the musical song, and wherein the forward timing information and the reverse timing information each indicate a start and end time of the line of the musical song, and wherein the method further comprises comparing the forward timing information and the reverse timing information by:

comparing the start time of the forward timing information with the start time of the reverse timing information to provide a start mismatch metric; and

comparing the end time of the forward timing information and the end time of the reverse timing information to provide an end mismatch metric.

5. The method of claim 1, further comprising:

for each of a plurality of portions of the vocal elements, determining timing boundary information;

computing a statistical model for a given duration of a portion of the vocal elements based on the plurality of portions of the vocal elements;

for each of a plurality of portions of the vocal elements, determining a probability that the duration fits the statistical model and comparing the probability to a threshold; and

for portions of the vocal elements that that have a probability lower than the threshold, marking the portion of the vocal elements with a low-confidence flag.

6. The method of claim 1, further comprising:

for each of a plurality of portions of the vocal elements, determining timing boundary information;

comparing the timing boundary information of each of the plurality of portions of the vocal elements amongst each other; and

based on the comparison, identifying outliers of the plurality of portions of the vocal elements.

7. The method of claim 1, wherein the audio signal comprises vocal elements and non-vocal elements, and the method further comprises suppressing the non-vocal elements.

8. The method of claim 1, wherein the audio signal is a song comprising lyrics, and wherein the method further comprises:

synchronizing the corresponding textual transcriptions of the vocal elements with the audio signal; and

outputting time-annotated synchronized lyrics that indicate timing information of lines of the lyrics in relation to the audio signal.

9. The method of claim 1, wherein performing the alignment comprises performing speech recognition on the vocal elements using a Viterbi decoder and Hidden Markov Models (HMM), and wherein the audio signal is a musical track by an artist, and the method further comprises:

accessing a database for synchronized lyrics of the artist;

adapting the HMM using the synchronized lyrics of the artist as adaptation data to produce an updated HMM; and

repeating the alignment using the updated HMM.

10. The method of claim 1, wherein the audio signal is a musical track, and wherein the portion of the vocal elements is selected from the group consisting of a line of lyrics of the musical tract and a word of lyrics of the musical track.

11. The method of claim 10, wherein outputting the confidence metric indicating a level of certainty for the timing boundary information for the duration of the portion of the vocal elements comprises marking the line of lyrics of the musical tract as a high or low confidence line.

12. The method of claim 1, further comprising:

performing speech recognition on the vocal elements to create a sequence of phonetic transcriptions; and

performing an alignment of the vocal elements with the phonetic transcriptions.

13. The method of claim 12, wherein performing the alignment comprises:

receiving lyrics text corresponding to the audio signal;

determining grammars for the lyrics text; and

mapping the phonetic description of the vocal elements to the grammars for the lyrics text.

14. The method of claim 1, wherein performing the alignment comprises performing speech recognition on a plurality of portions of the vocal elements using a Hidden Markov Model (HMM), and wherein the method further comprises:

making a determination whether the confidence metric exceeds a predetermined threshold; and

adapting the HMM using data comprising portions of the vocal elements that have a confidence metric that does not exceed the predetermined threshold to produce an updated HMM; and

repeating the alignment using the updated HMM.

15. The method of claim 14, further comprising repeating the steps of performing the alignment, outputting the confidence metric, adapting the HMM, and repeating the alignment in an iterative manner until there is no further decrease in a number of low-confidence lines.

16. The method of claim 1, wherein performing the alignment comprises performing speech recognition on the vocal elements using a Hidden Markov Model (HMM), and the method further comprises:

training a database of HMMs on training data based on metadata information of the audio signal; and

selecting an HMM to perform the alignment based on metadata information of the audio signal.

17. The method of claim 16, wherein the metadata information indicates information selected from the group consisting of a genre, an artist, a gender, and a tempo.

18. The method of claim 1, further comprising performing the alignment a plurality of times using different Hidden Markov Model (HMM) for each alignment;

determining the timing boundary information for each respective alignment;

determining the confidence metric for each respective alignment;

selecting an alignment that has a confidence metric indicating a highest level of certainty for the timing boundary information; and

outputting time-annotated synchronized lyrics that indicate timing boundary information corresponding to the selected alignment, wherein the timing boundary information pertains to lines of lyrics in relation to the audio signal.

19. A computer readable storage medium having stored therein instructions executable by a computing device to cause the computing device to perform functions of:

receiving an audio signal comprising vocal elements;

performing an alignment of the vocal elements with corresponding textual transcriptions of the vocal elements;

based on the alignment, determining timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements; and

outputting a confidence metric indicating a level of certainty for the timing boundary information for the duration of the portion of the vocal elements.

20. The computer readable storage medium of claim 19, wherein the function of performing the alignment of the vocal elements with corresponding textual transcriptions of the vocal elements comprises performing a forward alignment of the vocal elements processed in a forward direction with corresponding textual transcriptions of the vocal elements, wherein the function of determining timing boundary information comprises determining forward timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements processed in the forward direction, and wherein the instructions are further executable to perform functions of:

performing a reverse alignment of the vocal elements processed in a reverse direction with corresponding reverse textual transcriptions of the vocal elements;

determining reverse timing boundary information associated with an elapsed amount of time for a duration of the portion of the vocal elements processed in the reverse direction; and

based on a comparison between the forward timing information and the reverse timing information, outputting the confidence metric indicating a level of certainty for the forward timing boundary information.

21. The computer readable storage medium of claim 20, wherein the instructions are further executable to perform functions of:

determining a difference between the forward timing boundary information and the reverse timing boundary information;

making a comparison of the difference to a predefined threshold; and

based on the comparison, marking the portion of the vocal elements with a confidence level.

22. The computer readable storage medium of claim 20, wherein the audio signal is a musical song, and wherein the portion of the vocal elements is a line of the musical song, and wherein the forward timing information and the reverse timing information each indicate a start and end time of the line of the musical song, and wherein the instructions are further executable to perform functions of comparing the forward timing information and the reverse timing information by:

comparing the start time of the forward timing information with the start time of the reverse timing information to provide a start mismatch metric; and

comparing the end time of the forward timing information and the end time of the reverse timing information to provide an end mismatch metric.

23. The computer readable storage medium of claim 19, wherein the instructions are further executable to perform functions of:

for each of a plurality of portions of the vocal elements, determining timing boundary information;

computing a mean value of the timing boundary information for the plurality of portions of the vocal elements;

for each of a plurality of portions of the vocal elements, determining whether the duration of the portion of the vocal elements differs from the mean value by more than a threshold; and

for portions of the vocal elements that differ from the mean value by more than a threshold, marking the portion of the vocal elements with a low-confidence probability.

24. The computer readable storage medium of claim 19, wherein the audio signal is a song comprising lyrics, and wherein the instructions are further executable to perform functions of:

synchronizing the corresponding textual transcriptions of the vocal elements with the audio signal; and

outputting time-annotated synchronized lyrics that indicate timing information of lines of the lyrics in relation to the audio signal.

25. The computer readable storage medium of claim 19, wherein the function of performing the alignment comprises performing speech recognition on the vocal elements using a Hidden Markov Model (HMM), and wherein the instructions are further executable to perform functions of:

selecting an HMM based on metadata information of the audio signal.

26. A system comprising:

a Hidden Markov Model (HMM) database that includes phonetic modeling of words;

a pronunciation dictionary database that includes grammars representing words; and

a speech decoder that receives an audio signal and accesses the HMM to map vocal elements in the audio signal to phonetic descriptions and accesses the pronunciation dictionary database to map the phonetic descriptions to grammars, the speech decoder further performing an alignment of the grammars with corresponding textual transcriptions of the vocal elements,

wherein the speech decoder determines timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements, and the speech decoder determines a confidence metric indicating a level of certainty for the timing boundary information for the duration of the portion of the vocal elements.

27. The system of claim 26, wherein the speech decoder receives the audio signal in a forward direction and a reverse direction and accesses the HMM to map vocal elements in the audio signal to phonetic descriptions in both the forward direction and the reverse direction and accesses the pronunciation dictionary database to map the phonetic descriptions to grammars in both the forward and the reverse direction, the speech decoder further performing the alignment of the grammars with corresponding textual transcriptions of the vocal elements in both the forward direction and the reverse direction, and

wherein the speech decoder determines forward timing boundary information associated with an elapsed amount of time for a duration of a portion of the vocal elements processed in the forward direction and reverse timing boundary information associated with an elapsed amount of time for a duration of the portion of the vocal elements processed in the reverse direction, and based on a comparison between the forward timing boundary information and the reverse boundary timing information, the speech decoder determines the confidence metric indicating the level of certainty for the forward timing boundary information.

28. The system of claim 27, further comprising a grammar processor for receiving text corresponding to lyrics of the audio signal, and for determining grammars corresponding to the lyrics, wherein the speech decoder performs the alignment of the grammars with corresponding textual transcriptions of the vocal elements in both the forward direction and the reverse direction by aligning the grammars of the audio signal with the grammars of the lyrics.

29. The system of claim 27, wherein the speech decoder determines a difference between the forward timing information and the reverse timing information, and based on a comparison of the difference to a predefined threshold, the speech decoder marks the portion of the vocal elements with a confidence level.

30. The system of claim 26, wherein the speech decoder synchronizes textual transcriptions of the vocal elements with the audio signal, and outputs time-annotated synchronized lyrics that indicate timing boundary information of lines of lyrics in relation to the audio signal.