Sound sample verification for generating sound detection model

A method for verifying at least one sound sample to be used in generating a sound detection model in an electronic device includes receiving a first sound sample; extracting a first acoustic feature from the first sound sample; receiving a second sound sample; extracting a second acoustic feature from the second sound sample; and determining whether the second acoustic feature is similar to the first acoustic feature.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to verifying sound samples in an electronic device, and more specifically, to verifying sound samples for generating a sound detection model in an electronic device.

DESCRIPTION OF RELATED ART

In recent years, electronic devices such as smartphones, tablet computers, wearable electronic devices, and the like are becoming increasingly popular among consumers. These devices typically provide voice and/or data communication functionalities over wireless or wired networks. In addition, such electronic devices often include a speech recognition function for receiving and recognizing voice commands from a user. Such a function allows an electronic device to perform a function associated with a voice command (e.g., a keyword) when a voice command from the user is received and recognized. For example, the electronic device may activate a voice assistant application, play an audio file, or take a picture in response to a voice command from the user.

In electronic devices having a speech recognition feature, manufacturers or carriers often equip the devices with predetermined keywords and associated sound models, which may be used in detecting the keywords in an input sound. These sound models are typically generated based on a large number of sound recordings (e.g., thousands of speech samples or more) of keywords from different speakers in various sound environments. Such sound models provided by the manufacturers or carriers may be refined by a user of an electronic device. For example, an electronic device may receive several utterances of a predetermined keyword from the user and train a pre-stored sound model associated with the predetermined keyword using the utterances. Some electronic devices may also allow a user to designate a keyword as a voice command. In this case, electronic devices may receive several utterances of the designated keyword from a user and generate a sound model for the designated keyword from the utterances.

In general, the detection performance of a sound model is related to the number and quality of utterances from which the sound model is generated or trained. The detection performance of the sound model may improve as the number of utterances increases. For a given number of utterances for a keyword, however, the quality of the utterances may vary between speakers or between utterances. For example, if an utterance is received from an unintended user, the detection performance of the keyword model generated from the utterance may be degraded in detecting the keyword in an input sound from an intended user. On the other hand, if a user pronounces the keyword differently in two utterances, the keyword may not be detected correctly from the sound model generated from the utterances. Further, some utterances may be received in a noisy sound environment and thus may not provide sufficient quality for generating a sound model. Thus, the sound model generated or trained from such utterances may not produce adequate detection performance.

SUMMARY OF THE INVENTION

The present disclosure relates to verifying sound samples for generating a sound detection model in an electronic device.

According to one aspect of the present disclosure, a method for verifying at least one sound sample to be used in generating a sound detection model is disclosed. The method may be performed in an electronic device. In this method, a first sound sample may be received. A first acoustic feature may be extracted from the first sound sample. Further, a second sound sample may be received. A second acoustic feature may be extracted from the second sound sample. Whether the second acoustic feature is similar to the first acoustic feature is determined. This disclosure also describes an apparatus, a device, a system, a combination of means, and a computer-readable medium relating to this method.

According to another aspect of the present disclosure, an electronic device for verifying at least one sound sample to be used in generating a sound detection model is disclosed. The electronic device may include a sound sensor and a sound sample verification unit. The sound sensor may be configured to receive a first sound sample and a second sound sample. The sound sample verification unit may be configured to extract a first acoustic feature from the first sound sample; extract a second acoustic feature from the second sound sample; and determine whether the second acoustic feature is similar to the first acoustic feature.

DETAILED DESCRIPTION

Reference will now be made in detail to various embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present subject matter. However, it will be apparent to one of ordinary skill in the art that the present subject matter may be practiced without these specific details. In other instances, well-known methods, procedures, systems, and components have not been described in detail so as not to unnecessarily obscure aspects of the various embodiments.

FIG. 1illustrates an electronic device100configured to verify a plurality of sound samples S1, S2, S3, S4, and S5for generating a sound detection model, according to one embodiment of the present disclosure. The electronic device100may be any suitable device equipped with a sound capturing and processing capability such as a smartphone, a cellular phone, a personal computer, a laptop computer, a tablet computer, a smart television, a gaming device, a multimedia player, or the like. The electronic device100may receive the sound samples S1, S2, S3, S4, and S5from a user120for use in generating the sound detection model.

Although the electronic device100is illustrated as receiving five sound samples S1, S2, S3, S4, and S5, any suitable number of sound samples may be received and processed for generating a sound detection model and the number of sound samples may be predetermined based on a number of factors such as a user's convenience, preference, performance requirement, and the like. In one embodiment, the sound samples S1, S2, S3, S4, and S5may be received one after another and then processed in a batch mode. In another embodiment, the sound samples S1, S2, S3, S4, and S5may be received and processed one at a time as will be described in more detail with reference toFIG. 2below.

As used herein, the term “sound detection model” may refer to a model or a database used in detecting a keyword and/or a specific user in sound signals or data of an input sound received by the electronic device100and may include one or more acoustic features or characteristics indicative of the keyword and/or user. For example, a sound detection model may be a keyword detection model associated with or indicative of a keyword and adapted to detect the keyword in an input speech received from a user. The term “keyword” may refer to any digital or analog sound representation of one or more words that can be used to activate, operate, or control a function or an application in the electronic device100. Additionally or alternatively, the sound detection model may be a speaker verification model adapted to recognize a user from an input sound.

In one embodiment, the acoustic features may include spectral features, time domain features, or the like, which may be indicative of a keyword or a specific user. The spectral features may include mel frequency cepstral coefficients (MFCC), cepstrum difference coefficients (delta MFCC), line spectral pair (LSP) coefficients, or the like. The time domain features may include a zero crossing rate, a frame energy, or the like. The acoustic features may also include statistical measures of spectral features or time domain features such as a mean, a median, a mode, a variance, standard deviations, a covariance, a maximum value, a minimum value, a kurtosis, a high order momentum, etc., which may be indicative of a keyword or a specific user. In another embodiment, the acoustic features may include a sequence of subwords which may be indicative of a keyword or a specific user. As used herein, the term “subword” or “subword unit” may refer to a basic sound unit such as a phone, a phoneme, a triphone, a syllable, etc. Additionally or alternatively, the acoustic features may include a sound intensity level, a signal-to-noise ratio (SNR), or a reverberation time (RT), which may be indicative of sound quality.

The electronic device100may be configured to store one or more keywords for activating or performing one or more associated functions or applications in the electronic device100. The keywords may be one or more predetermined keywords (e.g., keywords assigned by a manufacturer) and/or one or more user-defined keywords. A keyword detection model for a predetermined keyword may be used in detecting the predetermined keyword in an input sound received by the electronic device100. According to one embodiment, a plurality of keyword detection models for a plurality of predetermined keywords may be generated by a third-party provider or a manufacturer of the electronic device100and pre-stored in the electronic device100and/or downloaded from an external server or device (not shown).

In the illustrated embodiment, the electronic device100may receive the sound samples S1, S2, S3, S4, and S5indicative of a predetermined keyword “Hey Snapdragon”130from the user120. A pre-stored keyword detection model associated with the predetermined keyword may be adapted to the user120by training the keyword detection model based on the sound samples S1, S2, S3, S4, and S5from the user120. The electronic device100may then use the trained keyword detection model adapted to the user120for detecting the predetermined keyword in an input sound.

In an additional or alternative embodiment, the user120may also define one or more user-defined keywords associated with one or more functions or applications to be activated or performed in the electronic device100. For example, the electronic device100may receive the sound samples S1, S2, S3, S4, and S5from the user120as sound samples indicative of a user-defined keyword. As used herein, the term “user-defined keyword” may refer to a keyword that may be defined or designated by the user120for activating or performing a function or an application of the electronic device100. Based on the received sound samples S1, S2, S3, S4, and S5, the electronic device100may generate a keyword detection model for the user-defined keyword that is adapted to the user120. The electronic device100may then use the generated keyword detection model adapted to the user120for detecting the user-defined keyword in an input sound.

Additionally or alternatively, a sound detection model may include a speaker verification model adapted to recognize the user120from an input sound. To generate a speaker verification model for recognizing the user120, the electronic device100may request the user120to speak a keyword, such as a predetermined keyword or a user-defined keyword, a specified number of times, which may be predetermined. When the user120provides the requested number of sound samples (e.g., the sound samples S1, S2, S3, S4, and S5), the electronic device100may generate a speaker verification model for the user120based on the provided sound samples. The electronic device100may then use the generated speaker verification model adapted to the user120for recognizing an input sound as being associated with the user120.

Once the sound samples S1, S2, S3, S4, and S5for generating a sound detection model are received, the electronic device100may determine whether each of the received sound samples S1, S2, S3, S4, and S5may be used in generating the sound detection model. In this process, one or more acoustic features may be extracted from each of the sound samples S1, S2, S3, S4, and S5. In one embodiment, the electronic device100may determine whether the acoustic features of each pair of the sound samples S1, S2, S3, S4, and S5are similar by comparing the associated extracted acoustic features. If the acoustic features of at least two sound samples are determined to be similar to each other, the electronic device100may determine that the at least two sound samples are similar and may be used in generating the sound detection model. On the other hand, if an acoustic feature of a sound sample is determined not to be similar to acoustic features of at least two other sound samples, the sound sample may not be used in generating the sound detection model.

In some embodiments, a pair of sound samples may be selected from the sound samples S1, S2, S3, S4, and S5and an acoustic feature may be extracted from each of the selected sound samples. The electronic device100may then determine whether the acoustic features of the selected sound samples are similar to each other. For example, the electronic device100may select the sound samples S1and S2and determine whether the acoustic features of the sound samples S1and S2are similar to each other. If the acoustic features of the sound samples S1and S2are determined to be similar, the electronic device100may determine that the sound samples S1and S2are similar and may be used in generating the sound detection model.

In one embodiment, the electronic device100may determine whether each of the remaining sound samples S3, S4, and S5may be used in generating the sound detection model based on acoustic features of the sound samples S1and S2. For example, the acoustic features of the sound samples S1and S2may be combined to generate a combined acoustic feature for the sound samples S1and S2. The electronic device100may select the sound sample S3and extract an acoustic feature from the sound sample S3. The combined acoustic feature of the sound samples S1and S2may then be compared with the acoustic feature extracted from the sound sample S3. If the combined acoustic feature and the acoustic feature of the sound sample S3are determined to be similar, the electronic device100may determine that the sound sample S3may be used in generating the sound detection model. The electronic device100may determine whether the sound samples S4and S5may be used in generating the sound detection model based on the combined acoustic features of two or more sound samples. For example, the sound sample S4may be processed based on the combined acoustic feature of the sound samples S1and S2or a combined acoustic feature of the sound samples S1, S2, and S3.

As used herein, the term “similar acoustic features” or equivalent variations thereof may mean that the acoustic features are the same or substantially the same within a specified tolerance or threshold value or percentage in feature values or parameters such as spectral features, time domain features, statistical measures, subwords, or the like. For example, in the case of two sequences of subwords in acoustic features, the electronic device100may determine that the two sequences are similar if a percentage of identical subwords in the sequences exceeds a threshold value. In one embodiment, two acoustic features may be determined to be similar when they vary by less than 30%.

In one embodiment, the electronic device100may determine a sound intensity level (e.g., an average sound intensity value) of each of the sound samples S1, S2, S3, S4, and S5as an acoustic feature. If the sound intensity level of a sound sample is determined to be less than a threshold sound level, it may indicate that the sound sample is not a good sound sample. Accordingly, the electronic device100may determine that the sound sample may not be used in generating a sound detection model.

Additionally or alternatively, the electronic device100may determine an SNR of each of the sound samples S1, S2, S3, S4, and S5as an acoustic feature. If an SNR of a sound sample is determined to be less than a threshold SNR, it may indicate that the sound sample has too much noise. Thus, the electronic device100may determine that the sound sample may not be used in generating a sound detection model.

Upon determining whether the sound samples S1, S2, S3, S4, and S5may be used in generating a sound detection model, the electronic device100may output an indication of whether the sound samples S1, S2, S3, S4, and S5may be used for generating a sound detection model. In the illustrated embodiment, for the predetermined keyword “Hey Snapdragon”130, the electronic device100may receive the five sound samples S1, S2, S3, S4, and S5for the keyword from the user120. For each of the sound samples S1, S2, S3, S4, and S5, the electronic device100may determine whether the sound sample may be used in generating a sound detection model. For example, the electronic device100may determine that the sound samples S1, S2, S4, and S5may be used in generating a sound detection model as shown inFIG. 1.

On the other hand, the electronic device100may determine that the sound sample S3may not be used in generating a sound detection model. For example, upon determining that a plurality of spectral features of the sound sample S3are not similar to the corresponding spectral features of each of the sound samples S1, S2, S4, and S5, the electronic device100may determine that the users associated with the sound sample S3and the sound samples S1, S2, S4, and S5are different and thus may determine that the sound sample S3may not be used in generating a sound detection model.

In one embodiment, the electronic device100may display a list136of the sound samples S1, S2, S3, S4, and S5indicating whether each of the sound samples S1, S2, S3, S4, and S5may be used in generating a sound detection model on a display screen110. For example, the list136may indicate that the sound samples S1, S2, S4, and S5are good sound samples that may be used for generating a sound detection model by displaying each of the samples S1, S2, S4, and S5in a box with a check mark. On the other hand, the sound sample S3, which may not be a good sound sample, may be displayed in the list136in a circle. Further, a message138indicating that the sound sample S3is associated with a different user may be displayed on the display screen110. Additionally or alternatively, a frowning face132indicating that the sound sample S3is not a good sound sample and thus may not be used in generating a sound detection model may be displayed on the display screen110along with a message134requesting the user120to input an additional sound sample for the sound sample S3.

In some embodiments, the message138may include a brief description of one or more reasons that a sound sample may not be used in generating a sound detection model. For example, the message138may include one or more reasons indicating that speaker characteristics of the sound sample are different from speaker characteristics of the other sound samples, a recognized keyword of the sound sample is different from recognized keywords of other sound samples, a sound intensity level of the sound sample is lower than a threshold sound intensity level, an SNR of the sound sample is lower than a threshold SNR, or the like.

The electronic device100may also be configured to determine a highest quality sound sample among the sound samples S1, S2, S3, S4, and S5based on the associated acoustic features. The highest quality sound sample may be used to generate a sequence of subwords for a sound detection model. In this embodiment, an icon140(e.g., a clickable bar) may be associated with a command to play a highest quality sound sample as a best sound sample and displayed on the display screen110. When the user120presses the icon140, the electronic device100may play the best sound sample, which may be referenced by the user120for inputting one or more additional sound samples. Additionally or alternatively, the electronic device100may receive an input from the user120identifying one of the sound samples S1, S2, S3, S4, and S5as a best sound sample and generate a sequence of subwords for a sound detection model based on the identified sound sample. Although the electronic device100is configured to display the icon140for playing a best sound sample, it may also be configured to allow the user120to select any sound sample among the sound samples S1to S5in the list136and play the selected sound sample.

In one embodiment, the electronic device100may display a plurality of icons142and144for inputting one or more substitute or additional sound samples. The icon “Redo Previous”142may indicate reentering the previously received sound sample for the user120. When the user120presses the icon142, the electronic device100may discard the previously received sound sample and receive a new sound sample from the user120as a replacement sample for the discarded sound sample. On the other hand, the icon “Start Over”144may indicate entering new sound samples for the sound samples S1to S5, which may be discarded. For example, if none of the sound samples S1to S5are determined to be used in generating a sound detection model or if the user120wants to enter new sound samples for the sound samples S1to S5, the user120may input the new sound samples by pressing the icon144. Upon receiving the new sound samples, the electronic device100may determine whether the new sound samples may be used in generating a sound detection model and display the results for the new sound samples on the display screen110.

According to some embodiments, the electronic device100may also be configured to receive an input from the user120selecting one or more sound samples S1, S2, S3, S4, and S5that may be used in generating a sound detection model. For example, the user120may select the sound samples S1, S2, and S4for use in generating a sound detection model. Additionally or alternatively, the electronic device100may receive an input from the user120selecting one or more sound samples to be replaced by new sound samples. For example, the user120may select the sound sample S4, which may have been determined to be used in generating a sound detection model by the electronic device100, to be replaced with a new sound sample. In this manner, the electronic device100may receive and process one or more new sound samples for the sound samples S1, S2, S3, S4, and S5based a selection by the user120.

When the sound samples S1, S2, S3, S4, and S5are determined to be used in generating a sound detection model, the electronic device100may generate a sound detection model based on at least one of the sound sample S1, S2, S3, S4, or S5. For example, the electronic device100may generate a sound detection model based on the acoustic features of the sound samples S1, S2, S3, S4, and S5. In this case, the acoustic features may be the same acoustic features as the acoustic features that have been used for determining whether the sound samples may be used in generating a sound detection model. Additionally or alternatively, the electronic device100may extract acoustic features from the sound samples that are adapted to or suitable for generating a sound detection model.

In one embodiment, the electronic device100may use at least one sound sample, which may have been rejected or discarded, or replaced with a new sound sample due to a low SNR, as a noise-embedded sound sample in adjusting or modifying the generated sound detection model. For example, in addition to or in lieu of using artificially generated noise samples, the sound sample that may have been rejected or discarded may be used as a noise-embedded sound sample in adjusting the generated sound detection model. Using one or more such sound samples may improve the detection performance of the sound detection model in various sound environments including a substantially noise-free environment and a noisy environment.

Additionally or alternatively, the electronic device100may use at least one sound sample, which may have been discarded due to a low SNR, a low sound intensity level, etc., to adjust a threshold value (e.g., a detection or similarity threshold value) for the sound detection model. For example, when an input sound indicative of a keyword is received, a plurality of sound detection models stored in the electronic device100may be accessed to identify a sound detection model associated with the keyword based on the threshold value. For identifying the sound detection model, a degree of similarity between the input sound and each of the keywords in the plurality of sound detection models may be determined. The electronic device100may then identify a sound detection model, which has a greatest similarity and whose degree of similarity is greater than or equal to the threshold value, as the sound detection model for the keyword. In one embodiment, the threshold value for the sound detection model may be determined based on at least one of the sound samples that have been determined to be used in generating the sound detection model. To enhance detection accuracy for the sound detection model, the threshold value for the sound detection model may be adjusted based on at least one sound sample, which may have been discarded due to a low SNR, a low sound intensity level, etc. For instance, the electronic device100may adjust the threshold value for the sound detection model using one or more sound samples that may have speaker characteristics different from speaker characteristics of the sound samples S1, S2, S3, S4, and S5, an SNR below a predetermined threshold SNR, etc.

FIG. 2illustrates the electronic device100that may be configured to verify one or more sound samples S1, S2, S3, S4, and S5for generating a sound detection model, according to another embodiment of the present disclosure. In this embodiment, the sound samples S1, S2, S3, S4, and S5may be received and processed sequentially one at a time. In one embodiment, the electronic device100may extract an acoustic feature from each sound sample when the sound sample is received and verify whether the sound quality of the sound sample is greater than or equal to a threshold quality for use in generating a sound detection model based on the acoustic feature indicative of a sound quality. If it is determined that the sound quality of the sound sample is less than the threshold quality, the electronic device100may receive a new sound sample from the user120as a replacement sound sample until it is determined that the sound quality of the sound sample is greater than or equal to the threshold quality. The electronic device100may then proceed to determine whether the sound sample may be used in generating the sound detection model based on whether the acoustic features of the sound sample and one or more other sound samples are similar.

The electronic device100may initially receive the sound sample S1from the user120and extract an acoustic feature from the sound sample S1. The electronic device100may determine whether the sound quality of the sound sample S1is greater than or equal to a threshold quality for use in generating a sound detection model based on the acoustic feature indicative of a sound quality such as an SNR, a sound intensity level, or the like. If the electronic device100determines that the sound quality of the sound sample S1is less than a threshold quality, it may display the message134prompting the user120to reenter the sound sample S1until it is determined that the sound quality of the sound sample S1is greater than or equal to a threshold quality for use in generating a sound detection model.

When the electronic device100determines that the sound quality of the sound sample S1is greater than or equal to a threshold quality for use in generating a sound detection model, it may receive the next sound sample S2from the user120and extract an acoustic feature from the sound sample S2. The electronic device100may determine whether the acoustic features of the sound samples S1and S2are similar for use in generating a sound detection model and output the results on the display screen110. If the acoustic features of the sound samples S1and S2are determined to be similar, the electronic device100may generate a combined acoustic feature based on the acoustic features of the sound samples S1and S2. If the acoustic features of the sound samples S1and S2are determined not to be similar, the electronic device100may request the user120to input a new sound sample as a replacement sound sample for the sound sample S1and/or S2until it is determined that the acoustic features of the sound samples S1and S2are similar.

Upon determining that the acoustic features of the sound samples S1and S2are similar, the electronic device100may receive the next sound sample S3from the user120and extract an acoustic feature from the sound sample S3. The electronic device100may then determine whether the acoustic feature of the sound sample S3is similar to the combined acoustic feature of the sound samples S1and S2. Additionally or alternatively, the electronic device100may determine whether the acoustic features of each pair of the sound samples S1, S2, and S3are similar. For example, the electronic device100may determine whether the acoustic features of the sound samples S1and S3are similar and/or determine whether the acoustic features of the sound samples S2and S3are similar. The results for the sound samples S1, S2, and S3may be displayed on the display screen110as shown inFIG. 2. In this manner, the electronic device100may also proceed to sequentially receive the sound samples S4and S5and determine whether each of the sound samples S4and S5may be used in generating a sound detection model based on acoustic features or a combination of acoustic features associated with the previously received sound samples.

As shown in the illustrated embodiment, the electronic device100may display a list200of the sound samples S1, S2, and S3indicating whether each of the sound samples S1, S2, and S3may be used in generating a sound detection model on the display screen110. For example, the list200may indicate that the sound samples S1and S2are good sound samples that may be used in generating a sound detection model by displaying each of the samples S1and S2in a box with a check mark. On the other hand, the sound sample S3, which may not be a good sound sample, may be displayed in the list200in a circle. Further, the message138indicating that the sound sample S3is associated with a different user may also be displayed on the display screen110. Additionally or alternatively, the frowning face132indicating that the sound sample S3is not a good sound sample and thus may not be used in generating a sound detection model may be displayed on the display screen110along with the message134prompting the user120to input another sound sample for the sound sample S3. Further, an icon202on the display screen110may be associated with a command to play a sound sample that may be selected from the sound samples S1, S2, and S3. When the user120presses the icon202when the sound sample S3is selected, the electronic device100may play the sound sample S3.

The electronic device100may also display a plurality of icons142and144for inputting one or more substitute or additional sound samples. The icon “Redo Previous”142may indicate reentering the previously received sound sample (e.g., the sound sample S3). When the user120presses the icon142, the electronic device100may receive a new sound sample as a replacement sample for the discarded sound sample. On the other hand, the icon “Start Over”144may indicate entering new sound samples for the sound samples S1, S2, and S3that have been received. For example, if it is determined that none of the sound samples S1, S2, and S3are to be used in generating a sound detection model or if the user120wants to enter new sound samples for the sound samples S1, S2, and S3, the user120may input the new sound samples by pressing the icon144. Upon sequentially receiving each of the new sound samples S1, S2, S3, S4, and S5, the electronic device may determine whether each new sound sample may be used in generating a sound detection model and display the determination results for the new sound sample on the display screen110.

In the illustrated embodiment, the sound samples S4and S5may be illustrated by a dotted circle to indicate that they have not been received yet. Although the electronic device100is described with reference to the sound sample S3, it may also process each of the sound samples S1, S2, S4, and S5for generating a sound detection model in a manner described above with reference toFIG. 1and cumulatively display information for the sound sample and previously received sound samples.

The electronic device100may receive subsequent sound samples S4and S5and determine that each of the sound samples S1, S2, S3, S4, and S5may be used in generating a sound detection model. The results for the sound samples S1to S5may be displayed cumulatively on the display screen110. When it is determined that the sound samples S1, S2, S3, S4, and S5may be used in generating a sound detection model, the electronic device100may generate a sound detection model based on at least one of the sound sample S1, S2, S3, S4, or S5. For example, the electronic device100may generate a sound detection model based on the acoustic features of the sound samples S1, S2, S3, S4, and S5. In this case, the acoustic features may be the same acoustic features as the acoustic features that have been used for determining whether the sound samples may be used in generating a sound detection model. Additionally or alternatively, the electronic device100may extract acoustic features from the sound samples that are adapted to or suitable for generating a sound detection model. According to some embodiments, the electronic device100may also be configured to receive an input from the user120indicating whether each of the sound samples may be used in generating a sound detection model. In this case, one or more sound samples selected by the user120may be used in generating a sound detection model even if the electronic device100has determined that the sound samples may not be used in generating a sound detection model.

FIG. 3illustrates a block diagram of the electronic device100configured to verify one or more sound samples for generating a sound detection model, according to one embodiment of the present disclosure. The electronic device100may include a sound sensor300, an input/output (I/O) unit310, a communication unit320, and a storage unit330, and a processor340. The I/O unit310may include the display screen110and a speaker (not shown). The display screen110may be a touch display screen configured to receive a touch input from a user. The processor340may include a sound sample verification unit342, a sound detection model generation unit344, a sound detection unit346, and a voice assistant unit348. The processor340may be implemented using any suitable processing unit such as a central processing unit (CPU), an application processor, a microprocessor, or the like that can execute instructions or perform operations for the electronic device100.

The sound sensor300may be configured to receive an input sound stream from a user. As used herein, the term “sound stream” may refer to a sequence of one or more sound signals or sound data. The sound sensor300may include one or more microphones or any other types of sound sensors that can be used to receive, capture, sense, and/or detect a sound input to the electronic device100. In addition, the sound sensor300may employ any suitable software and/or hardware for performing such functions. The received input sound stream of a user may be stored in the storage unit330. The sound sensor300may provide the received input sound stream of a user to the processor340for processing.

In one embodiment, the sound sensor300may receive an input sound stream including one or more sound samples from a user. Upon receiving the input sound stream, the sound sensor300may detect each of the sound samples from the input sound stream by detecting a start and end points of each of the sound samples or isolated utterances in the input sound stream using any suitable endpoint detection algorithms. The detected sound samples may be extracted and stored in the storage unit330. The extracted sound samples may be provided to the processor340for processing.

The storage unit330may be configured to store data and instructions for operating the sound sensor300, the I/O unit310, the communication unit320, and the processor340. The storage unit330may also store an input sound stream received by the sound sensor300or one or more sound samples extracted from the input sound stream. The storage unit330may be implemented using any suitable storage or memory devices such as a random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, or a solid state drive (SSD).

The storage unit330may also store at least one subword model that may be pre-stored in the storage unit330and/or downloaded from an external server or device (not shown) via the communication unit320. In some embodiments, the subword model may be, but not limited to, a phone-based model, a phoneme-based model, a triphone-based model, a syllable-based model, or the like, according to the type of subwords represented by the subword model, and may include a list of subword units and one or more acoustic features for each subword unit.

The storage unit330may also store one or more sound detection models used in detecting a keyword and/or a specific user in sound signals or data in an input sound. For example, the sound detection models may include a speaker verification model adapted to recognize a user from an input sound. Additionally or alternatively, the sound detection models may include one or more keyword detection models for detecting predetermined keywords and/or one or more keyword detection models for detecting user-defined keywords. The keyword detection models for detecting predetermined keywords may be pre-stored in the storage unit330or downloaded from an external server or device (not shown) via the communication unit320. In one embodiment, a keyword detection model may include a sequence of subwords including a plurality of portions (i.e., a plurality of subwords or subword units), which can be determined from one or more sound samples indicative of a keyword. The keyword detection model may also include model parameters associated with each of a plurality of subwords in the sequence of subwords, and a threshold value for detecting a keyword.

In another embodiment, the keyword detection model may include a subword network. The subword network may include a plurality of nodes and a plurality of lines that can connect at least two nodes of the plurality of nodes. The keyword detection model may also include at least one graphical model such as a hidden Markov model (HMM), a semi-Markov model (SMM), or the like, which corresponds to a node of the subword network. The graphical model may include a number of states and parameters such as a transition probability, a state output probability, etc.

The sound sample verification unit342in the processor340may be configured to receive one or more sound samples from the sound sensor300or the storage unit330and determine whether each of the received sound samples may be used in generating a sound detection model. Additionally or alternatively, the sound sample verification unit342may receive an input sound stream including one or more sound samples from the sound sensor300or the storage unit330and extract each of the sound samples from the input sound stream using any suitable endpoint detection algorithms. In this case, the sound sample verification unit342may store the extracted sound samples in the storage unit330.

Once the sound samples are received or extracted, the sound sample verification unit342may extract one or more acoustic features from each of the sound samples to determine whether each of the sound samples may be used in generating a sound detection model. In one embodiment, the sound sample verification unit342may determine whether the acoustic features of the sound samples are similar for use in generating a sound detection model. In another embodiment, the sound sample verification unit342may determine whether the sound quality of each of the sound samples is greater than or equal to a threshold quality for use in generating a sound detection model based on the acoustic feature indicative of a sound quality (e.g., an SNR, a sound intensity level, etc.). In some embodiments, the sound sample verification unit342may be configured to determine a highest quality sound sample among the sound samples based on the associated acoustic features. The determined highest quality sound sample may be provided to the sound detection model generation unit344for generating a sequence of subwords for a sound detection model.

Additionally or alternatively, the sound sample verification unit342may receive an input from a user identifying one of the sound samples as a best sound sample via the I/O unit310. In this case, the identified sound sample may be provided to the sound detection model generation unit344for generating a sequence of subwords for a sound detection model. In some embodiments, the sound sample verification unit342may instruct the I/O unit310to play the best sound sample through a speaker of the I/O unit310. Additionally or alternatively, the sound sample verification unit342may receive an input from a user identifying one or more sound samples to be played via the I/O unit310and instruct the I/O unit310to play the identified sound samples through a speaker of the I/O unit310.

In some embodiments, the sound sample verification unit342may receive one or more substitute or additional sound samples from the sound sensor300or the storage unit330. For example, if the sound sample verification unit342determines that one or more previously received sound samples may not be used in generating a sound detection model, it may discard the one or more previously received sound samples and receive one or more new sound samples for the discarded sound samples via the sound sensor300. Additionally or alternatively, if none of the previously received sound samples are determined to be used in generating a sound detection model, the sound sample verification unit342may discard the previously received sound samples and receive one or more new sound samples via the sound sensor300.

According to one embodiment, the sound sample verification unit342may receive the sound samples one after another and then process the received sound samples in a batch mode. For example, when five sound samples are received, the sound sample verification unit342may determine whether all five sound samples may be used in generating a sound detection model and instruct the I/O unit310to output an indication on the determination results of all five sound samples. In another embodiment, the sound sample verification unit342may receive and process the sound samples one at a time.

The sound detection model generation unit344may be configured to receive one or more sound samples that have been determined to be used in generating a sound detection model from the sound sample verification unit342. The sound detection model generation unit344may then generate a sound detection model based on at least one of the received sound samples. The sound detection model may include a speaker verification model, a keyword detection model for a predetermined keyword or a user-defined keyword, or the like. The sound detection model generation unit344may determine a threshold value for the sound detection model based on at least one of the received sound samples that have been determined to be used in generating the sound detection model. In one embodiment, the sound detection model generation unit344may receive at least one sound sample, which may have been discarded or replaced with a new sound sample due to a low SNR, from the sound sample verification unit342to use the sound sample as a noise-embedded sound sample in adjusting the generated sound detection model. Additionally or alternatively, the sound detection model generation unit344may receive at least one sound sample, which may have been discarded due to a low SNR, a low sound intensity level, etc., from the sound sample verification unit342and use the sound sample in adjusting the threshold value for the sound detection model. The sound detection model generation unit344may store the generated sound detection model in the storage unit330.

The sound detection unit346may be configured to receive the input sound stream from the sound sensor300or the storage unit330and detect a keyword and/or a specific user in sound signals or data of the input sound stream based on at least one sound detection model stored in the storage unit330. For example, upon receiving the input sound stream indicative of a keyword, the sound detection unit346may sequentially extract a plurality of acoustic features from the input sound stream and determine a matching score for at least one keyword (including at least one user-defined keyword and at least one predetermined keyword) associated with at least one keyword detection model. The sound detection unit346may then identify a keyword detection model that has a greatest matching score and whose matching score is greater than or equal to the threshold value as the matched keyword detection model for the input sound stream.

Upon identifying the matched keyword detection model associated with the keyword, the sound detection unit346may perform a function associated with the keyword or activate, control, or operate an application associated with the keyword. Additionally or alternatively, the sound detection unit346may generate and transmit an activation signal to turn on the voice assistant unit348, which may be associated with the keyword. The voice assistant unit348may be activated in response to the activation signal from the sound detection unit346. Once activated, the voice assistant unit348may perform a voice assistant function by outputting a message such as “MAY I HELP YOU?” on the display screen110and/or through a speaker of the I/O unit310. In response, a user may speak voice commands to activate various associated functions of the electronic device100. For example, when a voice command for Internet search is received, the voice assistant unit348may recognize the voice command as a search command and perform a web search via the communication unit320.

FIG. 4illustrates a block diagram of the sound sample verification unit342configured to determine whether one or more sound samples may be used in generating a sound detection model, according to one embodiment of the present disclosure. The sound sample verification unit342may include a feature extractor400, a feature buffer410, and a similarity determination unit420. The feature extractor400may be configured to receive one or more sound samples from the sound sensor300or the storage unit330and extract one or more acoustic features from each of the received sound samples. For example, the feature extractor400may extract spectral features, time domain features, or the like from each of the received sound samples. In one embodiment, the feature extractor400may perform subword recognition on each of the received sound samples to generate a sequence of subwords for each sound sample as an acoustic feature. In another embodiment, the feature extractor400may extract a sound intensity level (e.g., an average sound intensive level) or an SNR from each of the received sound samples.

In some embodiments, the feature extractor400may calculate an RT of each of the received sound samples. In this process, the feature extractor400may divide each of the sound samples into a plurality of sequential frames of an equal time period and extract one or more acoustic features (e.g., spectral features) from the frames. Based on the acoustic features in the frames, a similarity in one or more acoustic features among the plurality of frames may be determined as a correlation value. Based on the correlation values, the feature extractor400may determine a longest time period between two frames in a sound sample having a correlation value greater than or equal to a threshold correlation value as an RT of the sound sample.

Upon extracting the one or more acoustic features from each of the sound samples, the feature extractor400may store the extracted acoustic features in the feature buffer410. The feature buffer410may be configured to receive the extracted acoustic features from the feature extractor400and transmit them to the similarity determination unit420. The extracted acoustic features may also be stored in the storage unit330.

The similarity determination unit420may receive the extracted acoustic features from the feature buffer410or the storage unit330and determine whether the extracted acoustic features of the sound samples are similar. In one embodiment, the similarity determination unit420may determine whether the acoustic features of each pair of the sound samples are similar by comparing the associated acoustic features. If the acoustic features of at least two sound samples are determined to be similar, the similarity determination unit420may determine that the at least two sound samples are similar and may be used in generating the sound detection model. On the other hand, if the similarity determination unit420determines that an acoustic feature of a sound sample is not similar to acoustic features of at least two other sound samples, it may determine that the sound sample may not be used in generating the sound detection model.

In another embodiment, the similarity determination unit420may determine a combined acoustic feature of two or more sound samples and determine whether an acoustic feature of a sound sample is similar to the combined acoustic feature. The combined acoustic feature may be stored in the feature buffer410or the storage unit330. For example, if the acoustic features of the first and second sound samples are determined to be similar, the similarity determination unit420may determine a combined acoustic feature based on the acoustic features of the first and second sound samples. In this case, if an acoustic feature of the third sound sample is determined to be similar to the combined acoustic feature, the similarity determination unit420may determine that the first, second, and third sound samples are similar and may be used in generating the sound detection model. On the other hand, if the similarity determination unit420determines that an acoustic feature of the third sound sample is not similar to the combined acoustic feature, it may determine that the third sound sample may not be used in generating the sound detection model.

FIG. 5is a flow chart of a method500, performed in the electronic device100, for verifying one or more sound samples to be used in generating a sound detection model, according to one embodiment of the present disclosure. Initially, the electronic device100may receive a first sound sample for generating a sound detection model, at510. The electronic device100may extract a first acoustic feature from the first sound sample, at520. The electronic device100may receive a second sound sample for generating the sound detection model, at530. The electronic device100may extract a second acoustic feature from the second sound sample, at540. The electronic device100may determine whether the second acoustic feature is similar to the first acoustic feature, at550.

FIG. 6is a flow chart of a method600, performed in the electronic device100, for determining a combined acoustic feature, according to one embodiment of the present disclosure. Initially, the electronic device100may receive a first sound sample and extract a first acoustic feature from the first sound sample, at610. The electronic device100may receive a second sound sample and extract a second acoustic feature from the second sound sample, at620. The electronic device100may determine whether the second acoustic feature is similar to the first acoustic feature, at630. If the first and second acoustic features are determined to be similar (i.e., YES at640), the electronic device100may determine a combined acoustic feature based on the first and second acoustic features, at650. If the first and second acoustic features are determined not to be similar (i.e., NO at640), the method600may proceed to610for receiving the first sound sample and extract the first acoustic feature from the first sound sample.

FIG. 7is a flow chart of a method700, performed in the electronic device100, for verifying one or more sound samples to be used in generating a sound detection model, according to another embodiment of the present disclosure. As illustrated inFIG. 6, the electronic device100may determine the combined acoustic feature based on the first and second acoustic features. The electronic device100may receive a third sound sample for generating a sound detection model, at710. The electronic device100may extract a third acoustic feature from the third sound sample, at720. The electronic device100may determine whether the third acoustic feature is similar to the combined acoustic feature of the first and second acoustic features, at730. If the third acoustic feature is determined to be similar to the combined acoustic feature (i.e., YES at740), the electronic device100may generate the sound detection model based on the at least one the first sound sample, the second sound sample, or the third sound sample, at750. If the third acoustic feature is determined not to be similar to the combined acoustic feature (i.e., NO at740), the method700may proceed to710for receiving a third sound sample.

FIG. 8is a flow chart of a method800, performed in the electronic device100, for verifying one or more sound samples to be used in generating a sound detection model in a batch mode, according to one embodiment of the present disclosure. The electronic device100may receive a plurality of sound samples for generating a sound detection model, at810. The electronic device100may determine whether the received sound samples are similar, at820. If the sound samples are determined to be similar (i.e., YES at830), the electronic device100may generate the sound detection model based on the at least one of the sound samples, at850. If the sound samples are determined not to be similar (i.e., NO at830), the electronic device100may receive at least one new sound sample as a replacement sound sample for at least one sound sample that has been determined not to be similar to other sound samples, at840, and the method800may proceed to820for determining whether the received sound samples are similar.

FIG. 9is a flow chart of the method820, performed in the electronic device100, for determining whether acoustic features of one or more sound samples are similar, according to one embodiment of the present disclosure. The electronic device100may extract one or more acoustic features from each of the sound samples, at900. The electronic device100may determine whether the acoustic features of each pair of the sound samples are similar, at910. The electronic device100may output an indication on whether the sound samples are similar based on determining whether the acoustic features of each pair of the sound samples are similar, at920.

FIG. 10illustrates a block diagram of an exemplary electronic device1000in a wireless communication system in which the methods and apparatus of the present disclosure for verifying one or more sound samples to be used in generating a sound detection model may be implemented according to some embodiments. The exemplary electronic device1000may be a cellular phone, a smartphone, a wearable computer, a smart watch, smart glasses, a tablet personal computer, a terminal, a handset, a personal digital assistant (PDA), a wireless modem, a cordless phone, a tablet, and so on. The wireless communication system may be a CDMA system, a GSM system, a W-CDMA system, a LTE system, a LTE Advanced system, and so on.

The exemplary electronic device1000may be capable of providing bidirectional communication via a receive path and a transmit path. On the receive path, signals transmitted by base stations may be received by an antenna1012and may be provided to a receiver (RCVR)1014. The receiver1014may condition and digitize the received signal, and provide the conditioned and digitized digital signal to a digital section for further processing. On the transmit path, a transmitter (TMTR)1016may receive data to be transmitted from a digital section1020, process and condition the data, and generate a modulated signal, which is transmitted via the antenna1012to the base stations. The receiver1014and the transmitter1016may be part of a transceiver that may support CDMA, GSM, W-CDMA, LTE, LTE Advanced, and so on.

The digital section1020may include various processing, interface, and memory units such as, for example, a modem processor1022, a reduced instruction set computer/digital signal processor (RISC/DSP)1024, a controller/processor1026, an internal memory1028, a generalized audio/video encoder1032, a generalized audio decoder1034, a graphics/display processor1036, and an external bus interface (EBI)1038. The modem processor1022may perform processing for data transmission and reception, e.g., encoding, modulation, demodulation, and decoding. The RISC/DSP1024may perform general and specialized processing for the exemplary electronic device1000. The controller/processor1026may perform the operation of various processing and interface units within the digital section1020. The internal memory1028may store data and/or instructions for various units within the digital section1020.

The generalized audio/video encoder1032may perform encoding for input signals from an audio/video source1042, a microphone1044, an image sensor1046, etc. The generalized audio decoder1034may perform decoding for coded audio data and may provide output signals to a speaker/headset1048. The graphics/display processor1036may perform processing for graphics, videos, images, and texts, which may be presented to a display unit1050. The EBI1038may facilitate transfer of data between the digital section1020and a main memory1052.

The digital section1020may be implemented with one or more processors, DSPs, microprocessors, RISCs, etc. The digital section1020may also be fabricated on one or more application specific integrated circuits (ASICs) and/or some other type of integrated circuits (ICs).

FIG. 11is a block diagram illustrating a server system1100, which may be any one of the servers previously described implemented according to some embodiments. The server system1100may include one or more processing units (e.g., CPUs)1102, one or more network or other communications network interfaces, a memory1112, and one or more communication buses1114for interconnecting these components. The server system1100may also include a user interface (not shown) having a display device and a keyboard.

The memory1112may be any suitable memory, such as a high-speed random access memory, (e.g., DRAM, SRAM, DDR RAM or other random access solid state memory devices). The memory1112may include or may alternatively be non-volatile memory (e.g., one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices). In some embodiments, the memory1112may include one or more storage devices remotely located from the CPU(s)1102and/or remotely located in multiple sites.

Any one of the above memory devices represented by the memory1112may store any number of modules or programs that corresponds to a set of instructions for performing and/or executing any of the processes, operations, and methods previously described. For example, the memory1112may include an operating system1116configured to store instructions that includes procedures for handling various basic system services and for performing hardware dependent tasks. A network communication module1118of the memory1112may be used for connecting the server system1100to other computers via the one or more communication network interfaces1110(wired or wireless) and one or more communication networks, such as the Internet, other wide area networks, local area networks, metropolitan area networks, and so on.

The memory1112may also include a database1120configured to include an image database a plurality of images having one or more objects (e.g., a text object and a non-text object), a classifier database, a character information database, dictionary database, etc. The operating system1116may update the image database with various images, which may be received and/or captured, through the network communication module1118. The operating system1116may also provide the images to a plurality of electronic devices via the network communication module1118. In addition, the classifier database, the character information database, the dictionary database may be provided to a plurality of electronic device for use in detecting at least one text region of an image and/or recognize one or more character strings in the at least one text region.

In general, any device described herein may represent various types of devices, such as a wireless phone, a cellular phone, a laptop computer, a wireless multimedia device, a wireless communication personal computer (PC) card, a PDA, an external or internal modem, a device that communicates through a wireless channel, etc. A device may have various names, such as access terminal (AT), access unit, subscriber unit, mobile station, mobile device, mobile unit, mobile phone, mobile, remote station, remote terminal, remote unit, user device, user equipment, handheld device, etc. Any device described herein may have a memory for storing instructions and data, as well as hardware, software, firmware, or combinations thereof.

For a hardware implementation, the processing units used to perform the techniques may be implemented within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, a computer, or a combination thereof.

It will be appreciated that the above identified modules or programs (i.e., sets of instructions) need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. Furthermore, the memory1112may store additional modules and data structures not described above.

ASPECTS OF THE PRESENT DISCLOSURE

Hereinafter, some aspects of the present disclosure will be additionally stated.

According to an aspect of the present disclosure, there is provided a method for verifying at least one sound sample to be used in generating a sound detection model, the method comprising: receiving a first sound sample; extracting a first acoustic feature from the first sound sample; receiving a second sound sample; extracting a second acoustic feature from the second sound sample; and determining whether the second acoustic feature is similar to the first acoustic feature.

The method of Example 1 further comprises generating the sound detection model based on at least one of the first sound sample or the second sound sample, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The method of Example 1 or 2 further comprises determining a signal-to-noise ratio (SNR) of the first sound sample; determining an SNR of the second sound sample; selecting at least one of the first sound sample or the second sound sample based on the SNRs of the first and second sound samples; generating the sound detection model based on the selected at least one sound sample upon determining that the second acoustic feature is similar to the first acoustic feature; and adjusting the generated sound detection model based on the unselected sound sample.

The method of any one of Examples 1 to 3 further comprises determining a combined acoustic feature based on the first acoustic feature and the second acoustic feature, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The method of any one of Examples 1 to 4 further comprises receiving a third sound sample; extracting a third acoustic feature from the third sound sample; and determining whether the third acoustic feature is similar to the combined acoustic feature.

In the method of any one of Examples 1 to 5, each of the first acoustic feature and the second acoustic feature includes at least one of spectral features or time domain features.

In the method of any one of Examples 1 to 6, each of the first acoustic feature and the second acoustic feature includes a sequence of subwords.

In the method of any one of Examples 1 to 7, the subwords include at least one of phones, phonemes, triphones, or syllables.

The method of any one of Examples 1 to 8 further comprises receiving a new sound sample in response to determining that the second acoustic feature is not similar to the first acoustic feature; extracting a new acoustic feature from the new sound sample; and determining whether the new acoustic feature is similar to the first acoustic feature.

The method of any one of Examples 1 to 9 further comprises generating the sound detection model based on at least one of the first sound sample or the new sound sample in response to determining that the new acoustic feature is similar to the first acoustic feature.

In the method of any one of Examples 1 to 10, generating the sound detection model comprises determining a threshold value of the sound detection model based on at least one of the first sound sample or the new sound sample; and adjusting the threshold value based on the second sound sample.

The method of any one of Examples 1 to 11 further comprises outputting an indication on whether the second sound sample is similar to the first sound sample based on determining whether the second acoustic feature is similar to the first acoustic feature; and receiving an input indicative of at least one of the first sound sample or the second sound sample to be used in generating the sound detection model.

In the method of any one of Examples 1 to 12, each of the first sound sample and the second sound sample is indicative of at least one of a command to activate the electronic device or a command to control an application or a function in the electronic device.

The method of any one of Examples 1 to 13 further comprises receiving a speech input; and recognizing at least one of a keyword or a user from the speech input based on the sound detection model.

According to another aspect of the present disclosure, there is provided an electronic device for verifying at least one sound sample to be used in generating a sound detection model, comprising: a sound sensor configured to receive a first sound sample and a second sound sample; and a sound sample verification unit configured to extract a first acoustic feature from the first sound sample, extract a second acoustic feature from the second sound sample, and determine whether the second acoustic feature is similar to the first acoustic feature.

The electronic device of Example 15 further comprises a sound detection model generation unit configured to generate the sound detection model based on at least one of the first sound sample or the second sound sample, in response to determining that the second acoustic feature is similar to the first acoustic feature.

In the electronic device of Example 15 or 16, the sound sample verification unit is configured to determine a combined acoustic feature based on the first acoustic feature and the second acoustic feature, in response to determining that the second acoustic feature is similar to the first acoustic feature.

In the electronic device of any one of Examples 15 to 17, the sound sensor is configured to receive a third sound sample; and the sound sample verification unit is configured to extract a third acoustic feature from the third sound sample and determine whether the third acoustic feature is similar to the combined acoustic feature.

In the electronic device of any one of Examples 15 to 18, the sound sensor is configured to receive a new sound sample in response to determining that the second acoustic feature is not similar to the first acoustic feature, and the sound sample verification unit is configured to extract a new acoustic feature from the new sound sample and determine whether the new acoustic feature is similar to the first acoustic feature.

According to still another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium comprising instructions causing at least one processor of an electronic device to perform operations of: receiving a first sound sample; extracting a first acoustic feature from the first sound sample; receiving a second sound sample; extracting a second acoustic feature from the second sound sample; and determining whether the second acoustic feature is similar to the first acoustic feature.

The non-transitory computer-readable storage medium of Example 20 further comprises instructions causing the at least one processor of the electronic device to perform operations of generating the sound detection model based on at least one of the first sound sample or the second sound sample, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The non-transitory computer-readable storage medium of Example 20 or 21 further comprises instructions causing the at least one processor of the electronic device to perform operations of determining a combined acoustic feature based on the first acoustic feature and the second acoustic feature, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The non-transitory computer-readable storage medium of any one of Examples 20 to 22 further comprises instructions causing the at least one processor of the electronic device to perform operations of: receiving a third sound sample; extracting a third acoustic feature from the third sound sample; and determining whether the third acoustic feature is similar to the combined acoustic feature.

The non-transitory computer-readable storage medium of any one of Examples 20 to 23 further comprises instructions causing the at least one processor of the electronic device to perform operations of: receiving a new sound sample in response to determining that the second acoustic feature is not similar to the first acoustic feature; extracting a new acoustic feature from the new sound sample; and determining whether the new acoustic feature is similar to the first acoustic feature.

According to yet another aspect of the present disclosure, there is provided an electronic device for verifying at least one sound sample to be used in generating a sound detection model, comprising: means for receiving a first sound sample; means for extracting a first acoustic feature from the first sound sample; means for receiving a second sound sample; means for extracting a second acoustic feature from the second sound sample; and means for determining whether the second acoustic feature is similar to the first acoustic feature.

The electronic device of Example 25 further comprises means for generating the sound detection model based on at least one of the first sound sample or the second sound sample, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The electronic device of Example 25 or 26 further comprises means for determining a combined acoustic feature based on the first acoustic feature and the second acoustic feature, in response to determining that the second acoustic feature is similar to the first acoustic feature.

The electronic device of any one of Examples 25 to 27 further comprises: means for receiving a third sound sample; means for extracting a third acoustic feature from the third sound sample; and means for determining whether the third acoustic feature is similar to the combined acoustic feature.

The electronic device of any one of Examples 25 to 28 further comprises: means for receiving a new sound sample in response to determining that the second acoustic feature is not similar to the first acoustic feature; means for extracting a new acoustic feature from the new sound sample; and means for determining whether the new acoustic feature is similar to the first acoustic feature.

The electronic device of any one of Examples 25 to 29 further comprises: means for outputting an indication on whether the second sound sample is similar to the first sound sample based on determining whether the second acoustic feature is similar to the first acoustic feature; and means for receiving an input indicative of at least one of the first sound sample or the second sound sample to be used in generating the sound detection model.