Multi-Talker Audio Stream Separation, Transcription and Diaraization

A plurality of talker embedding vectors may be derived that correspond to a plurality of talkers in an input audio stream. Each talker embedding vector may represent respective voice characteristics of a respective talker. The talker embedding vectors may be generated based on, for example, a pre-enrollment process or a cluster-based embedding vector derivation process. A plurality of instances of a personalized noise suppression model may be executed on the input audio stream. Each instance of the personalized noise suppression model may employ a respective talker embedding vector. A plurality of single-talker audio streams may be generated by the plurality of instances of the personalized noise suppression model. A plurality of single-talker transcriptions may be generated based on the plurality of single-talker audio streams. The plurality of single-talker transcriptions may be merged into a multi-talker output transcription.

BACKGROUND

Automatic speech transcription is a valuable feature for archiving and accessibility. For example, the use of speech transcription may allow a record of a meeting, a performance, or other events to be generated and preserved. Also, in some examples, speech transcription may allow a textual indication of speech to be provided to viewers that may have difficulty hearing or in scenarios in which listeners are otherwise unable to clearly hear the speech. In some cases, such as in some noisy conditions or multi-talker scenarios, the performance of speech transcription systems may be degraded. Some speech transcription systems may be trained for situations in which a single talker is active or when there is clear turn-taking between multiple talkers. Thus, in some situations in which multiple talkers are active concurrently, the performance of some speech transcription systems may be degraded. Furthermore, in the event that the transcription system is also configured for diarization, talker labeling may be compromised when multiple talkers are simultaneously active.

DETAILED DESCRIPTION

Techniques for multi-talker audio stream separation, transcription and diarization are described herein. An input audio stream may include speech of a plurality of talkers. According to the techniques described herein, a plurality of talker embedding vectors may be derived, with each of the talker embedding vectors corresponding to a respective talker of the plurality of talkers in the input audio stream. Each talker embedding vector may represent respective voice characteristics of the respective talker. Additionally, a plurality of instances of a personalized noise suppression model may be executed on the input audio stream. Specifically, each instance of the personalized noise suppression model may correspond to a respective talker of the plurality of talkers and may employ the respective talker embedding vector for its respective talker. The personalized noise suppression model may be a machine learning model that is trained to preserve a particular voice represented by an embedding vector. Thus, each instance of the personalized noise suppression model may employ its respective embedding vector to preserve speech corresponding to its respective talker and to suppress any signals other than the respective talker's speech.

The plurality of instances of the personalized noise suppression model may be executed on the input audio stream to generate a plurality of single-talker audio streams. Each single-talker audio stream of the plurality of single-talker audio streams may be generated by a respective instance of the plurality of instances of the personalized noise suppression model by outputting only sounds, from the input audio stream, that correspond to the respective talker embedding vector. Thus, each single-talker audio stream may include speech only from a respective talker—and no other talkers. The plurality of single-talker audio streams may be generated using a consistent time representation scheme (e.g., consistent time stamps) that is consistent across the plurality of single-talker audio streams.

The plurality of single-talker audio streams may be provided to a transcription system, which may generate a plurality of single-talker transcriptions from the plurality of single-talker audio streams. Each single-talker transcription of the plurality of single-talker transcriptions may be generated from a respective single-talker audio stream of the plurality of single-talker audio streams. Thus, each single-talker transcription may be a transcription of speech only from a respective talker—and no other talkers. Each of the single-talker transcriptions may be generated using the consistent time representation scheme of the underlying single-talker audio streams. The plurality of single-talker transcriptions may then be merged, based on the consistent time representation scheme, into a multi-talker output transcription corresponding to the input audio stream.

In some examples, the plurality of talker embedding vectors may be derived based on a pre-enrollment process for the plurality of talkers. The term pre-enrollment, as used herein, refers to an enrollment process for the plurality of talkers in which the plurality of talker embedding vectors are derived before the generation of the input audio stream is initiated and/or before the input audio stream is processed for transcription. During the pre-enrollment process, audio samples of speech of each of the plurality of talkers may be analyzed in order to derive the plurality of talker embedding vectors. Specifically, a set of one or more audio samples may be provided for each talker and may include speech only for that respective talker—and no other talkers. The talker embedding vector for the respective talker may then be derived based on the set of one or more audio samples. In some examples, identity data indicating an identity of the talker may be associated with both the respective set of one or more audio samples and the respective talker embedding vector that is derived from the respective set of one or more audio samples. The identity data may allow the talker's identity to be indicated in association with transcribed speech from the talker in both the talker's respective single-talker audio stream and in the multi-talker output transcription. In some cases, because it allows the plurality of talker embedding vectors to be derived before the input audio stream is generated, the pre-enrollment process may be particularly suitable for online applications in which live transcription is employed.

In some other examples, the plurality of talker embedding vectors may be derived based on a cluster-based embedding vector derivation. In the cluster-based embedding vector derivation, the talker embedding vectors are derived based on the input audio stream itself. Thus, unlike the pre-enrollment process, the cluster-based embedding vector derivation does not allow the plurality of talker embedding vectors to be derived before generation of the input audio stream is initiated. Rather, in the cluster-based embedding vector derivation, the plurality of talker embedding vectors are derived either after the input audio stream is fully generated (referred to hereinafter as a post-completion cluster-based embedding vector derivation) or while generation of the input audio stream is in-progress (referred to hereinafter as an in-progress cluster-based embedding vector derivation).

During the post-completion cluster-based embedding vector derivation, a talker-enumeration machine learning model may analyze the input audio stream in its entirety to estimate time-varying numbers of concurrent talkers within the input audio stream at a plurality of times. The time-varying numbers of concurrent talkers may then be used to determine a plurality of single-talker segments of the input audio stream in which there is only one talker. A plurality of segment embedding vectors may then be derived from the plurality of single-talker segments, in which one or more segment embedding vectors of the plurality of segment embedding vectors are derived for each single-talker segment of the plurality of single-talker segments. The plurality of segment embedding vectors may then be clustered into a plurality of embedding vector clusters. Each embedding vector cluster may correspond to a respective talker of the plurality of talkers. A plurality of representative embedding vectors may then be derived from the plurality of embedding vector clusters. Each representative embedding vector of the plurality of representative embedding vectors may be derived for a respective embedding vector cluster of the plurality of embedding vector clusters. The plurality of representative embedding vectors may then be used as the plurality of talker embedding vectors. Because the post-completion cluster-based embedding vector derivation is not performed until after the input audio stream is fully generated, the post-completion cluster-based embedding vector derivation may be particularly suitable for offline non-live applications.

The in-progress cluster-based embedding vector derivation may be similar to the post-completion cluster-based embedding vector derivation, with some exceptions. Specifically, while the post-completion cluster-based embedding vector derivation may be performed on the entire input audio stream after the input audio stream is fully generated, the in-progress cluster-based embedding vector derivation may be performed repeatedly, in a running fashion, during generation of the input audio stream. Specifically, the in-progress cluster-based embedding vector derivation may be performed repeatedly, in multiple iterations, on different portions of the input audio stream as those portions are generated. Thus, for the in-progress cluster-based embedding vector derivation, the number of talkers and the talker clustering may be estimated in a running fashion. Additionally, for the in-progress cluster-based embedding vector derivation, talker enumeration, talker clustering, and representative talker embeddings may be continuously improved (e.g., on each iteration of the derivation) so that the speech extraction performance improves as the stream progresses.

Thus, by separating an input audio stream into a plurality of single-talker audio streams, generating single-talker transcripts, and merging the single-talker audio transcripts into a multi-talker output transcript, the techniques described herein allow multi-talker speech separation, transcription and diarization. It is noted that the techniques described herein may be particularly advantageous for speech transcription and diarization in scenarios when background speech is present, such as call centers, events with audiences, and the like. The techniques described herein may also be particularly advantageous for speech transcription and diarization in scenarios in which multiple talkers are simultaneously active.

FIG.1is a diagram illustrating an example multi-talker audio stream separation, diarization and transcription system that may be used in accordance with the present disclosure. In the example ofFIG.1, an input audio stream100includes speech from three talkers, which include Ann, Bob and Carol. Input audio stream100may include audio from a wide variety of sources, for example including an online meeting, a phone call, a call center, a movie, a television show, an event (e.g., sporting event, news event, entertainment event, a performance, etc.), and/or other sources. In some examples, input audio stream100may be a live stream, which is captured and played to listeners with no appreciable delay between the time that the stream is captured and the time that the stream is played to listeners. In some other examples, input audio stream100may be a pre-recorded stream, which is recorded in advance, saved, and then played to listeners at a later time after the input audio stream has been fully generated.

As will be described in detail below, talker embedding vectors (TEV's)105A-C may be derived for the talkers in the input audio stream100. In this example, talker embedding vector (TEV)105A corresponds to Ann, TEV105B corresponds to Bob, and TEV105C corresponds to Carol. Each TEV105A-C may represent respective voice characteristics of the respective talker. Specifically, TEV105A represents voice characteristics of Ann, TEV105B represents voice characteristics of Bob, and TEV105C represents voice characteristics of Carol. Additionally, in this example, stream separation components150execute personalized noise suppression (PNS) instances101A-C on the input audio stream100. The PNS instances101A-C are instances of a personalized noise suppression model. Each PNS instance101A-C corresponds to a respective talker of the plurality of talkers in the input audio stream100. Specifically, PNS instance101A corresponds to Ann, PNS instance101B corresponds to Bob, and PNS instance101C corresponds to Carol. Additionally, each PNS instance101A-C employs one of TEV's105A-C for its respective talker. Specifically, PNS instance101A employs TEV105A, PNS instance101B employs TEV105B, and PNS instance101C employs TEV105C. The personalized noise suppression model may be a machine learning model that is trained to preserve a particular voice represented by an embedding vector. Thus, each PNS instance101A-C may employ a TEV105A-C, respectively, to preserve speech corresponding to its respective talker and to suppress any signals other than the respective talker's speech.

As shown inFIG.1, PNS instances101A-C may be executed on the input audio stream100to generate single-talker audio streams102A-C. Specifically, single-talker audio stream102A corresponds to Ann, single-talker audio stream102B corresponds to Bob, and single-talker audio stream102C corresponds to Carol. Each single-talker audio stream102A-C is generated by a respective one of the PNS instances101A-C by outputting only sounds, from the input audio stream100, that correspond to the respective one of the TEV's105A-C. Thus, each single-talker audio stream102A-C may include speech only from a respective talker—and no other talkers. Specifically, single-talker audio stream102A may include speech only from Ann, single-talker audio stream102B may include speech only from Bob, and single-talker audio stream102C may include speech only from Carol. The single-talker audio streams102A-C may be generated using a consistent time representation scheme (e.g., consistent time stamps) that is consistent across the single-talker audio streams102A-C.

As also shown inFIG.1, the single-talker audio streams102A-C may be provided to transcription components160, which may generate single-talker transcriptions103A-C from the single-talker audio streams102A-C. Each single-talker transcription103A-C is generated from a respective one of single-talker audio streams102A-C. Thus, each single-talker transcription103A-C may be a transcription of speech only from a respective talker—and no other talkers. Specifically, single-talker transcription103A may be a transcription of speech only from Alice, single-talker transcription103B may be a transcription of speech only from Bob, and single-talker transcription103C may be a transcription of speech only from Carol. Each of the single-talker transcriptions103A-C may be generated using the consistent time representation scheme of the underlying single-talker audio streams. For example, each of the single-talker transcriptions103A-C may include indications of words that were spoken by a respective talker and indications of the times that those words were spoken.

The transcription components160may then merge the single-talker transcriptions103A-C into merged transcription104. The merged transcription104is a multi-talker output transcription corresponding to the input audio stream100. The single-talker transcriptions103A-C may be merged into merged transcription104based on the consistent time representation scheme. For example, the merged transcription104may include indications of words that were spoken by each of the talkers in the input audio stream100(e.g., Alice, Bob and Carol), indications of the times that those words were spoken, and indications of the identity of the talkers that spoke those words. In some examples, the identity of the talkers may be determined based on the single-talker transcription103A-C from which the words are obtained. For example, it may be determined that words obtained from single-talker transcription103A are spoken by Ann, that words obtained from single-talker transcription103B are spoken by Bob, and that that words obtained from single-talker transcription103C are spoken by Carol.

Referring now toFIGS.2-4, some examples are shown of techniques that may be employed to generate TEV's105A-C. These example techniques may include pre-enrollment embedding vector derivation (see, e.g.,FIG.2), post-completion cluster-based embedding vector derivation (see, e.g.,FIG.3), and in-progress cluster-based embedding vector derivation (see, e.g.,FIG.4). Referring now toFIG.2, an example of pre-enrollment embedding vector (EV) derivation200will now be described. The term pre-enrollment, as used herein, refers to an enrollment process for the plurality of talkers in which the plurality of talker embedding vectors (e.g., TEV's105A-C) are derived before the generation of the input audio stream100is initiated and/or before the input audio stream100is processed for transcription. During the pre-enrollment embedding vector (EV) derivation200, audio samples201A-C of speech of each of the talkers may be analyzed in order to derive the plurality of TEV's105A-C. In this example, audio samples201A include samples of speech only of Ann (and no other talker), audio samples201B include samples of speech only of Bob (and no other talker), and audio samples201C include samples of speech only of Carol (and no other talker). TEV105A may be derived based on audio samples201A, TEV105B may be derived based on audio samples201B, and TEV105C may be derived based on audio samples201C. After the pre-enrollment embedding vector (EV) derivation200, the input audio stream may then be processed, as shown inFIG.1, by having PNS instances101A-C analyze the input audio stream100using the TEV's105A-C, respectively.

In some examples, as part of pre-enrollment embedding vector (EV) derivation200, identity data indicating an identity of a respective talker may be associated with both the audio samples201A-C and the TEV's105A-C that are derived from the audio samples201A-C. For example, identity data indicating the identity of Ann may be associated with audio samples201A and TEV105A, identity data indicating the identity of Bob may be associated with audio samples201B and TEV105B, and identity data indicating the identity of Carol may be associated with audio samples201C and TEV105C. The identity data may allow the talker's identity to be indicated in association with transcribed speech from the talker in both the single-talker transcriptions103A-C and in the merged transcription104. In some cases, because it allows the TEV's105A-C to be derived before the input audio stream100is generated, the pre-enrollment embedding vector (EV) derivation200may be particularly suitable for online applications in which live transcription is employed.

In some other examples, the TEV's105A-C may be derived based on a cluster-based embedding vector derivation. In the cluster-based embedding vector derivation, the TEV's105A-C are derived based on the input audio stream100itself. Thus, unlike the pre-enrollment embedding vector (EV) derivation200ofFIG.2, the cluster-based embedding vector derivation does not allow the TEV's105A-C to be derived before generation of the input audio stream100is initiated. Rather, in the cluster-based embedding vector derivation, the TEV's105A-C are derived either after the input audio stream is fully generated (referred to hereinafter as a post-completion cluster-based embedding vector derivation) or while generation of the input audio stream is in-progress (referred to hereinafter as an in-progress cluster-based embedding vector derivation).

Referring now toFIG.3, an example of post-completion cluster-based embedding vector derivation will now be described. As shown inFIG.3, during the post-completion cluster-based embedding vector derivation, a talker-enumeration machine learning (ML) model300may analyze the input audio stream100in its entirety to estimate time-varying numbers of concurrent talkers within the input audio stream100at a plurality of times. Specifically, the talker-enumeration ML model300may estimate a quantity of talkers that are speaking within the input audio stream100at any given time. The time-varying numbers of concurrent talkers may then be used to determine single-talker segments (STS's)311-316of the input audio stream100. Each single-talker segment (STS)311-316is a segment of the input audio stream100in which there is only one talker. An embedding vectorization310may then be performed on each STS311-316to derive segment embedding vectors (SEV's)301-308. Each STS311-316includes at least one of the SEV's301-308. In the example ofFIG.3, SEV's301and302are derived for STS311, segment embedding vector (SEV)303is derived for STS312, SEV's304and305are derived for STS313, SEV306is derived for STS314, SEV307is derived for STS315, and SEV308is derived for STS316. In this example, each SEV301-308is an embedding vector that represents the audio characteristics of a person that is talking in a respective one of the STS's311-316. For example, SEV's301and302may represent the audio characteristics of a person that is talking in STS311. SEV301may be generated based on speech in a first half of STS311, while SEV302may be generated based on speech in a second half of STS312. Thus, although they may represent the speech of the same person, SEV301and SEV302may be slightly different from one another, as a person's speech characteristics (e.g., tone, pitch, volume/amplitude, speed of talking, etc.) may change slightly over time.

An embedding vector clustering algorithm320may then be executed on the SEV's301-308to cluster the SEV's301-308into embedding vector clusters330A,330B and330C. In this example, embedding vector cluster330A includes SEV's301,302and306, embedding vector cluster330B includes SEV's304,305and307, and embedding vector cluster330C includes SEV's303and308. The embedding vector clustering algorithm320may cluster the SEV's301-308into the embedding vector clusters330A-C based on the relative similarities between the SEV's301-308. For example, SEV's301,302and306, which are clustered into embedding vector cluster330A, may have greater similarity to each other than to any of the SEV's303-305,307and308, which are not included in the embedding vector cluster330A. Similarly, SEV's304,305and307, which are clustered into embedding vector cluster330B, may have greater similarity to each other than to any of the SEV's301-303,306and308, which are not included in the embedding vector cluster330B. Additionally, SEV's303and308, which are clustered into embedding vector cluster330C, may have greater similarity to each other than to any of the SEV's301-302and304-307, which are not included in the embedding vector cluster330C.

Each embedding vector cluster330A-C may correspond to a respective talker of the plurality of talkers. Specifically, embedding vector cluster330A corresponds to Ann, embedding vector cluster330B corresponds to Bob, and embedding vector cluster330C corresponds to Carol. In this example, because there are three talkers in the input audio stream100(Ann, Bob and Carol), three embedding vector clusters330A-C are formed. It is noted that, for cluster-based embedding vector derivation, the number of talkers in the input audio stream100(and the number of embedding vector clusters330A-C that are formed via the clustering) are not known in advance. Rather, the number of talkers and embedding vector clusters330A-C are instead determined based on the result of the clustering, such as based on the relative similarities between the SEV's301-308.

During representative embedding vector (REV) derivation340, representative embedding vectors (REV's)331-333may be derived from the embedding vector clusters330A-C. Specifically, REV331A is derived from embedding vector cluster330A, REV331B is derived from embedding vector cluster330B, and REV331C is derived from embedding vector cluster330C. REV331A is an embedding vector that is representative of the SEV's301,302and306within the embedding vector cluster330A. For example, REV331A may be the centroid, or may be generated based on the centroid, of SEV's301,302and306. REV331B is an embedding vector that is representative of the SEV's304,305and307within the embedding vector cluster330B. For example, REV331B may be the centroid, or may be generated based on the centroid, of SEV's304,305and307. REV331C is an embedding vector that is representative of the SEV's303and308within the embedding vector cluster330C. For example, REV331C may be the centroid, or may be generated based on the centroid, of SEV's303and308. The REV's331A-C may then be used as TEV's105A-C for the respective talkers. Specifically, REV331A may be used as TEV105A for Ann, REV331B may be used as TEV105B for Bob, and REV331C may be used as TEV105C for Carol. Because the post-completion cluster-based embedding vector derivation is not performed until after the input audio stream100is fully generated, the post-completion cluster-based embedding vector derivation may be particularly suitable for offline non-live applications.

Referring now toFIG.4, an example of in-progress cluster-based embedding vector derivation will now be described in detail. In the example ofFIG.4, the in-progress cluster-based embedding vector derivation is performed on input audio stream400, which also includes audio of talkers Ann, Bob and Carol. Input audio stream400includes stream portions400A-B and optionally any number of additional stream portions (not shown inFIG.4). The in-progress cluster-based embedding vector derivation may be similar to the post-completion cluster-based embedding vector derivation, with some exceptions. Specifically, while the post-completion cluster-based embedding vector derivation may be performed on the input audio stream100, in its entirety, after the input audio stream100is fully generated, the in-progress cluster-based embedding vector derivation may be performed repeatedly, in a running fashion, as the input audio stream400is being generated. Specifically, the in-progress cluster-based embedding vector derivation may be performed repeatedly, in multiple iterations (e.g., cluster-based EV derivation iterations401-402), on different portions (e.g., stream portions400A-B) of the input audio stream400as those portions are generated. Thus, for the in-progress cluster-based embedding vector derivation, the number of talkers and the talker clustering may be estimated in a running fashion. Additionally, for the in-progress cluster-based embedding vector derivation, talker enumeration, talker clustering, and representative talker embeddings may be continuously improved (e.g., on each iteration of the derivation) so that the speech extraction performance improves as the stream progresses.

Each of the cluster-based EV derivation iterations401-402ofFIG.4may be performed by executing the actions described above with reference toFIG.3. Specifically, each of the cluster-based EV derivation iterations401-402may include analyzing a respective one of the stream portions400A-B using talker enumeration ML model300to estimate time-varying numbers of concurrent talkers within the respective one of the stream portions400A-B at a plurality of times. The time-varying numbers of concurrent talkers may then be used to determine a plurality of single-talker segments of the respective one of the stream portions400A-B in which there is only one talker. During embedding vectorization310, a plurality of segment embedding vectors may then be derived from the plurality of single-talker segments. The embedding vector clustering algorithm320may then be performed to cluster the plurality of segment embedding vectors into one or more embedding vector clusters. Each embedding vector cluster may correspond to a respective talker of the plurality of talkers. The representative embedding vector derivation340may then be performed to derive one or more representative embedding vectors from the one or more embedding vector clusters. The one or more representative embedding vectors may then be used as one or more of the TEV's105A-C.

For example, as shown inFIG.4, cluster-based EV derivation iteration401is performed on stream portion400A. In this example, stream portion400A includes only one single-talker segment (STS), which is STS411. As shown, STS411includes audio of Ann. Thus, cluster-based EV derivation iteration401provides only representative embedding vector (REV)431A-1, which is an initial REV corresponding to Ann. By contrast, stream portion400B includes four STS's, which are STS's411-414. As shown, STS411includes audio of Ann, STS412includes audio of Bob, STS413includes additional audio of Ann, and STS414includes audio of Carol. Accordingly, cluster-based EV derivation402provides REV431A-2, REV431B-1and REV431C-1. Specifically, REV431B-1is an initial REV corresponding to Bob, REV431C-1is an initial REV corresponding to Carol, and REV431A-2is an improved REV corresponding to Ann. Thus, while cluster-based EV derivation iteration401reflected only a single talker (Ann), cluster-based EV derivation402reflects three talkers (Ann, Bob and Carol). Moreover, cluster-based EV derivation402also generates a more accurate representation of Ann's voice. Specifically, while REV431A-1was generated based only on STS411(which corresponds to Ann), REV431A-2is generated based on both STS411and STS413(which both correspond to Ann). Thus, because it is generated based on a greater number of samples of Ann's voice, REV431A-2is an improved REV that more accurately represents Ann's voice than REV431A-1.

In the example ofFIG.3described above, talker enumeration ML model300is executed on the input audio stream to determine STS's311-316. The embedding vectorization310is then performed only on the STS's311-316to determine SEV's301-308. However, in some other examples, it may not be necessary to execute the talker enumeration ML model300prior to embedding vectorization310. Referring now toFIG.5, an example is shown of an alternative technique in which the talker enumeration ML model300is not executed prior to embedding vectorization310. Rather, in the alternative technique ofFIG.5, embedding vectorization310is performed on an entire input audio stream500(or a portion thereof) and not only on single-talker segments. In this alternative technique, the embedding vectors corresponding to single-talker segments are detected during the clustering process and are then discarded, meaning that they are not used to determine representative embedding vectors.

As shown inFIG.5, embedding vectorization310is performed on input audio stream500to generate nine segment embedding vectors (SEV's)501-509. Specifically, SEV's501-503correspond to a first portion of the input audio stream500in which only Ann is speaking. Additionally, SEV's504-506correspond to a second portion of the input audio stream500in which both Ann and Bob are speaking. Furthermore, SEV's507-509correspond to a third portion of the input audio stream500in which only Bob is speaking.

In this example, embedding vector clustering algorithm520is executed on the SEV's501-509. The embedding vector clustering algorithm520clusters SEV's501-503into embedding vector cluster530A corresponding to Ann. Additionally, the embedding vector clustering algorithm520clusters SEV's507-509into embedding vector cluster530B corresponding to Bob. Furthermore, the embedding vector clustering algorithm520detects that SEV's504-506correspond to portions of the input audio stream500in which multiple talkers (e.g., Ann and Bob) are speaking simultaneously. The embedding vector clustering algorithm520groups SEV's504-506into discarded multi-talker group535. A variety of techniques may be employed to detect that SEV's504-506correspond to portions of the input audio stream500in which multiple talkers (e.g., Ann and Bob) are speaking simultaneously. In some examples, the embedding vector clustering algorithm520may examine the temporal (time-based) relationships between SEV's501-509. Specifically, the embedding vector clustering algorithm520may examine neighborhoods of SEV's, which include two or more adjacent SEV's in succession (or otherwise in close time proximity to one another), to determine the amount of similarity or correlation between the adjacent SEV's. For example, for scenarios in which only a single person is speaking in adjacent SEV's, it is expected that those adjacent SEV's will have a high degree of similarity to one another since they correspond to speech from the same person. By contrast, for scenarios in which multiple talkers are speaking simultaneously in adjacent SEV's, it is expected that those SEV's will not have a high degree of similarity since they correspond to speech from more than one person. In the example ofFIG.5, it may be determined that adjacent SEV's501-503have a high degree of similarity to one another and therefore correspond to the same person (Ann). Additionally, it may be determined that adjacent SEV's507-509also have a high degree of similarity to one another and therefore correspond to the same person (Bob). By contrast, it may be determined that adjacent SEV's504-506do not have a high degree of similarity to one another and therefore correspond to a portion of the input audio stream500in which multiple talkers (Ann and Bob) are speaking simultaneously.

As also shown inFIG.5, a representative embedding vector (REV)531A is determined for embedding vector cluster530A and is used as TEV105A for Ann. Additionally, an REV531B is determined for embedding vector cluster530B and is used as TEV105B for Bob. It is noted, however, that no REV is determined for discarded multi-talker group535. It is noted that the alternative technique ofFIG.5may be employed for both post-completion cluster-based embedding vector derivation and, also, for in-progress cluster-based embedding vector derivation.

FIG.6is a flowchart illustrating an example multi-talker audio stream separation, diarization and transcription process that may be used in accordance with the present disclosure. At operation610, a plurality of talker embedding vectors are derived. The plurality of talker embedding vectors may correspond to a plurality of talkers in an input audio stream. Each talker embedding vector of the plurality of talker embedding vectors may correspond to a respective talker of the plurality of talkers and may represent respective voice characteristics of the respective talker. For example, as shown inFIG.1, input audio stream100includes speech from three talkers, which include Ann, Bob and Carol. Talker embedding vectors (TEV's)105A-C may be derived for the talkers in the input audio stream100. In this example, TEV105A corresponds to Ann, TEV105B corresponds to Bob, and TEV105C corresponds to Carol. Each TEV105A-C may represent respective voice characteristics of the respective talker. Specifically, TEV105A represents voice characteristics of Ann, TEV105B represents voice characteristics of Bob, and TEV105C represents voice characteristics of Carol.

Some example techniques for derivation of the TEV's105A-C are described in detail above and are not repeated here. Specifically, the deriving of the plurality of talker embedding vectors may include performing an enrollment process in which the plurality of talkers are enrolled, such as pre-enrollment EV derivation200ofFIG.2. Additionally, the deriving of the plurality of talker embedding vectors may include performing a cluster-based embedding vector derivation. In some examples, the cluster-based embedding vector derivation may be performed after the input audio stream is fully generated and may be performed on all of the input audio stream (e.g., the post-completion cluster-based embedding vector derivation ofFIG.3). Also, in some examples, the cluster-based embedding vector derivation may be performed in a running fashion during generation of the input audio stream and may be performed repeatedly on different portions of the input audio stream as the different portions are generated (e.g., the in-progress cluster-based embedding vector derivation ofFIG.4). Moreover, the alternative technique shown inFIG.5may optionally be employed for both post-completion cluster-based embedding vector derivation and in-progress cluster-based embedding vector derivation.

At operation612, a plurality of instances of a personalized noise suppression model are executed on the input audio stream. Each instance of the plurality of instances of the personalized noise suppression model employs a respective talker embedding vector of the plurality of talker embedding vectors. For example, as shown inFIG.1, stream separation components150execute personalized noise suppression (PNS) instances101A-C on the input audio stream100. The PNS instances101A-C are instances of a personalized noise suppression model. Each PNS instance101A-C corresponds to a respective talker of the plurality of talkers in the input audio stream100. Specifically, PNS instance101A corresponds to Ann, PNS instance101B corresponds to Bob, and PNS instance101C corresponds to Carol. Additionally, each PNS instance101A-C employs one of TEV's105A-C for its respective talker. Specifically, PNS instance101A employs TEV105A, PNS instance101B employs TEV105B, and PNS instance101C employs TEV105C. The personalized noise suppression model may be a machine learning model that is trained to preserve a particular voice represented by an embedding vector. Thus, each PNS instance101A-C may employ a TEV105A-C, respectively, to preserve speech corresponding to its respective talker and to suppress any signals other than the respective talker's speech.

At operation614, a plurality of single-talker audio streams are generated by the plurality of instances of the personalized noise suppression model. The plurality of single-talker audio streams may correspond to the input audio stream. Each single-talker audio stream of the plurality of single-talker audio streams may be generated by a respective instance of the plurality of instances of the personalized noise suppression model by outputting only sounds, from the input audio stream, that correspond to the respective talker embedding vector. The plurality of single-talker audio streams may be generated using a consistent time representation scheme that is consistent across the plurality of single-talker audio streams. For example, as shown inFIG.1, PNS instances101A-C may be executed on the input audio stream100to generate single-talker audio streams102A-C. Specifically, single-talker audio stream102A corresponds to Ann, single-talker audio stream102B corresponds to Bob, and single-talker audio stream102C corresponds to Carol. Each single-talker audio stream102A-C is generated by a respective one of the PNS instances101A-C by outputting only sounds, from the input audio stream100, that correspond to the respective one of the TEV's105A-C. Thus, each single-talker audio stream102A-C may include speech only from a respective talker—and no other talkers. Specifically, single-talker audio stream102A may include speech only from Ann, single-talker audio stream102B may include speech only from Bob, and single-talker audio stream102C may include speech only from Carol. The single-talker audio streams102A-C may be generated using a consistent time representation scheme (e.g., consistent time stamps) that is consistent across the single-talker audio streams102A-C.

At operation616, a plurality of single-talker transcriptions are generated based on the plurality of single-talker audio streams. Each single-talker transcription of the plurality of single-talker transcriptions may be generated based on a respective single-talker audio stream of the plurality of single-talker audio streams. For example, as shown inFIG.1, the single-talker audio streams102A-C may be provided to transcription components160, which may generate single-talker transcriptions103A-C from the single-talker audio streams102A-C. Each single-talker transcription103A-C is generated from a respective one of single-talker audio streams102A-C. Thus, each single-talker transcription103A-C may be a transcription of speech only from a respective talker—and no other talkers. Specifically, single-talker transcription103A may be a transcription of speech only from Alice, single-talker transcription103B may be a transcription of speech only from Bob, and single-talker transcription103C may be a transcription of speech only from Carol. Each of the single-talker transcriptions103A-C may be generated using the consistent time representation scheme of the underlying single-talker audio streams. For example, each of the single-talker transcriptions103A-C may include indications of words that were spoken by a respective talker and indications of the times that those words were spoken.

At operation618, the plurality of single-talker transcriptions are merged into a multi-talker output transcription. The plurality of single-talker transcriptions may be merged into the multi-talker output transcription based on the consistent time representation scheme. The multi-talker output transcription may correspond to the plurality of talkers. For example, as shown inFIG.1, the transcription components160may merge the single-talker transcriptions103A-C into merged transcription104. The merged transcription104is a multi-talker output transcription corresponding to the input audio stream100. The single-talker transcriptions103A-C may be merged into merged transcription104based on the consistent time representation scheme. For example, the merged transcription104may include indications of words that were spoken by each of the talkers in the input audio stream100(e.g., Alice, Bob and Carol), indications of the times that those words were spoken, and indications of the identity of the talkers that spoke those words. In some examples, the identity of the talkers may be determined based on the single-talker transcription103A-C from which the words are obtained. For example, it may be determined that words obtained from single-talker transcription103A are spoken by Ann, that words obtained from single-talker transcription103B are spoken by Bob, and that that words obtained from single-talker transcription103C are spoken by Carol. Thus, an identity of a first talker of the plurality of talkers that spoke a first portion of speech in the multi-talker output transcription may be indicated in the multi-talker output transcription. The identity of the first talker may be indicated based on a first single-talker transcription of the plurality of single talker transcriptions from which the first portion of speech is obtained.

FIG.7is a flowchart illustrating an example cluster-based embedding vector derivation process that may be used in accordance with the present disclosure. Operations710-718may be included in a cluster-based embedding vector derivation process. In some examples, operations710-718ofFIG.7may be included in a post-completion cluster-based embedding vector derivation process, such as described above with reference toFIG.3. As described above, when included in a post-completion cluster-based embedding vector derivation process, operations710-718may be performed a single time on an entire input audio stream after input audio stream has been fully generated. In some other examples, operations710-718ofFIG.7may be included in an in-progress cluster-based embedding vector derivation process, such as described above with reference toFIG.4. As described above, when included in an in-progress cluster-based embedding vector derivation process, operations710-718may be performed in a running fashion during generation of the input audio stream and may be performed repeatedly on different portions of the input audio stream as the different portions are generated. For example, operations710-718may be performed initially within cluster-based EV derivation iteration401ofFIG.4(on stream portion400A) and then may be performed again within cluster-based EV derivation iteration401ofFIG.4(on stream portion400B).

At operation710, at least part of the input audio stream is analyzed, using a talker-enumeration model, to estimate time-varying numbers of concurrent talkers within the input audio stream at a plurality of times. For example, as shown inFIG.3, during the post-completion cluster-based embedding vector derivation, a talker-enumeration ML model300may analyze the input audio stream100in its entirety to estimate time-varying numbers of concurrent talkers within the input audio stream100at a plurality of times. Specifically, the talker-enumeration ML model300may estimate a quantity of talkers that are speaking within the input audio stream100at any given time. As also described above, for in-progress cluster-based EV derivation, different portions of the input audio stream (e.g., stream portion400A, stream portion400B, etc.) may be analyzed in a running fashion.

At operation712, a plurality of single-talker segments of the input audio stream may be determined using the time-varying numbers of concurrent talkers. The plurality of single-talker segments of the input audio stream are segments in which there is only one talker. For example, as shown inFIG.3, the time-varying numbers of concurrent talkers may be used to determine single-talker segments (STS's)311-316of the input audio stream100. Each single-talker segment (STS)311-316is a segment of the input audio stream100in which there is only one talker.

At operation714, a plurality of segment embedding vectors are derived. One or more segment embedding vectors of the plurality of segment embedding vectors may be derived for each single-talker segment of the plurality of single-talker segments. For example, as shown inFIG.3, an embedding vectorization310may be performed on each STS311-316to derive segment embedding vectors (SEV's)301-308. Each STS311-316includes at least one of the SEV's301-308. In the example ofFIG.3, SEV's301and302are derived for STS311, segment embedding vector (SEV)303is derived for STS312, SEV's304and305are derived for STS313, SEV306is derived for STS314, SEV307is derived for STS315, and SEV308is derived for STS316. Each SEV301-308is an embedding vector that represents the audio characteristics of a person that is talking in a respective one of the STS's311-316. For example, SEV's301and302may represent the audio characteristics of a person that is talking in STS311. SEV301may be generated based on speech in a first half of STS311, while SEV302may be generated based on speech in a second half of STS312. Thus, although they may represent the speech of the same person, SEV301and SEV302may be slightly different from one another, as a person's speech characteristics (e.g., tone, pitch, volume/amplitude, speed of talking, etc.) may change slightly over time.

At operation716, the plurality of segment embedding vectors are clustered into a plurality of embedding vector clusters. For example, as shown inFIG.3, an embedding vector clustering algorithm320may be executed on the SEV's301-308to cluster the SEV's301-308into embedding vector clusters330A,330B and330C. The embedding vector clustering algorithm320may cluster the SEV's301-308into the embedding vector clusters330A-C based on the relative similarities between the SEV's301-308. For example, SEV's301,302and306, which are clustered into embedding vector cluster330A, may have greater similarity to each other than to any of the SEV's303-305,307and308, which are not included in the embedding vector cluster330A. Similarly, SEV's304,305and307, which are clustered into embedding vector cluster330B, may have greater similarity to each other than to any of the SEV's301-303,306and308, which are not included in the embedding vector cluster330B. Additionally, SEV's303and308, which are clustered into embedding vector cluster330C, may have greater similarity to each other than to any of the SEV's301-302and304-307, which are not included in the embedding vector cluster330C.

Each embedding vector cluster330A-C may correspond to a respective talker of the plurality of talkers. Specifically, embedding vector cluster330A corresponds to Ann, embedding vector cluster330B corresponds to Bob, and embedding vector cluster330C corresponds to Carol. In this example, because there are three talkers in the input audio stream100(Ann, Bob and Carol), three embedding vector clusters330A-C are formed. It is noted that, for cluster-based embedding vector derivation, the number of talkers in the input audio stream100(and the number of embedding vector clusters330A-C that are formed via the clustering) are not known in advance. Rather, the number of talkers and embedding vector clusters330A-C are instead determined based on the result of the clustering, such as based on the relative similarities between the SEV's301-308.

At operation718, a plurality of representative embedding vectors are derived for the plurality of embedding vector clusters, wherein the plurality of talker embedding vectors includes the plurality of representative embedding vectors. Each representative embedding vector of the plurality of representative embedding vectors may be derived for a respective embedding vector cluster of the plurality of embedding vector clusters. For example, as shown inFIG.3, during representative embedding vector (REV) derivation340, REV's331-333may be derived from the embedding vector clusters330A-C. Specifically, REV331A is derived from embedding vector cluster330A, REV331B is derived from embedding vector cluster330B, and REV331C is derived from embedding vector cluster330C. REV331A is an embedding vector that is representative of the SEV's301,302and306within the embedding vector cluster330A. For example, REV331A may be the centroid, or may be generated based on the centroid, of SEV's301,302and306. REV331B is an embedding vector that is representative of the SEV's304,305and307within the embedding vector cluster330B. For example, REV331B may be the centroid, or may be generated based on the centroid, of SEV's304,305and307. REV331C is an embedding vector that is representative of the SEV's303and308within the embedding vector cluster330C. For example, REV331C may be the centroid, or may be generated based on the centroid, of SEV's303and308. The REV's331A-C may then be used as TEV's105A-C for the respective talkers. Specifically, REV331A may be used as TEV105A for Ann, REV331B may be used as TEV105B for Bob, and REV331C may be used as TEV105C for Carol.

It is noted that operations710and712ofFIG.7relate to examples in which a talker enumeration ML model is executed (prior to embedding vectorization) to determine single-talker segments, and the embedding vectorization is performed only on the single-talker segments. However, as also described above, an alternative technique (e.g., described above with reference toFIG.5) may optionally be employed in which it is not necessary to execute the talker enumeration ML model prior to embedding vectorization. As described above, in this alternative technique, the embedding vectors corresponding to single-talker segments are detected during the clustering process and are then discarded, meaning that they are not used to determine representative embedding vectors. Thus, for the alternative technique, operations710and712ofFIG.7may be skipped. For this reason, operations710and712are illustrated inFIG.7using dashed lines instead of solid lines. Additionally, for the alternative technique, segment embedding vectors may be determined for the entire input audio stream (or portion thereof) and not only for single-talker segments. It is noted that the alternative technique ofFIG.5may be employed for both post-completion cluster-based embedding vector derivation and, also, for in-progress cluster-based embedding vector derivation.

For the alternative technique ofFIG.5, the clustering performed at operation716may include discarding one or more of the plurality of segment embedding vectors corresponding to one or more multi-talker segments of the input audio stream, which are portions of the input audio stream in which multiple talkers are speaking simultaneously. As described above with reference toFIG.5, a variety of techniques may be employed to detect that SEV's504-506correspond to portions of the input audio stream500in which multiple talkers (e.g., Ann and Bob) are speaking simultaneously. In some examples, the embedding vector clustering algorithm520may examine the temporal (time-based) relationships between SEV's501-509. Specifically, the embedding vector clustering algorithm520may examine neighborhoods of SEV's, which include two or more adjacent SEV's in succession (or otherwise in close time proximity to one another), to determine the amount of similarity or correlation between the adjacent SEV's. For example, for scenarios in which only a single person is speaking in adjacent SEV's, it is expected that those adjacent SEV's will have a high degree of similarity to one another since they correspond to speech from the same person. By contrast, for scenarios in which multiple talkers are speaking simultaneously in adjacent SEV's, it is expected that those SEV's will not have a high degree of similarity since they correspond to speech from more than one person. In the example ofFIG.5, it may be determined that adjacent SEV's501-503have a high degree of similarity to one another and therefore correspond to the same person (Ann). Additionally, it may be determined that adjacent SEV's507-509also have a high degree of similarity to one another and therefore correspond to the same person (Bob). By contrast, it may be determined that adjacent SEV's504-506do not have a high degree of similarity to one another and therefore correspond to a portion of the input audio stream500in which multiple talkers (Ann and Bob) are speaking simultaneously. For the alternative technique, these time-based analysis techniques may be employed as part of operation716ofFIG.7. As also shown inFIG.5, a representative embedding vector (REV)531A is determined for embedding vector cluster530A and is used as TEV105A for Ann. Additionally, an REV531B is determined for embedding vector cluster530B and is used as TEV105B for Bob. It is noted, however, that no REV is determined for discarded multi-talker group535.

An example system for transmitting and providing data will now be described in detail. In particular,FIG.8illustrates an example computing environment in which the embodiments described herein may be implemented.FIG.8is a diagram schematically illustrating an example of a data center85that can provide computing resources to users70aand70b(which may be referred herein singularly as user70or in the plural as users70) via user computers72aand72b(which may be referred herein singularly as computer72or in the plural as computers72) via a communications network73. Data center85may be configured to provide computing resources for executing applications on a permanent or an as-needed basis. The computing resources provided by data center85may include various types of resources, such as gateway resources, load balancing resources, routing resources, networking resources, computing resources, volatile and non-volatile memory resources, content delivery resources, data processing resources, data storage resources, data communication resources and the like. Each type of computing resource may be available in a number of specific configurations. For example, data processing resources may be available as virtual machine instances that may be configured to provide various web services. In addition, combinations of resources may be made available via a network and may be configured as one or more web services. The instances may be configured to execute applications, including web services, such as application services, media services, database services, processing services, gateway services, storage services, routing services, security services, encryption services, load balancing services, application services and the like. These services may be configurable with set or custom applications and may be configurable in size, execution, cost, latency, type, duration, accessibility and in any other dimension. These web services may be configured as available infrastructure for one or more clients and can include one or more applications configured as a platform or as software for one or more clients. These web services may be made available via one or more communications protocols. These communications protocols may include, for example, hypertext transfer protocol (HTTP) or non-HTTP protocols. These communications protocols may also include, for example, more reliable transport layer protocols, such as transmission control protocol (TCP), and less reliable transport layer protocols, such as user datagram protocol (UDP). Data storage resources may include file storage devices, block storage devices and the like.

Data center85may include servers76aand76b(which may be referred herein singularly as server76or in the plural as servers76) that provide computing resources. These resources may be available as bare metal resources or as virtual machine instances78a-b(which may be referred herein singularly as virtual machine instance78or in the plural as virtual machine instances78). In this example, the resources also include audio stream processing virtual machines (ASPVM's)79a-b, which are virtual machines that are configured to execute any, or all, of the multi-talker audio stream separation, transcription and diarization techniques described above.

Referring toFIG.8, communications network73may, for example, be a publicly accessible network of linked networks and possibly operated by various distinct parties, such as the Internet. In other embodiments, communications network73may be a private network, such as a corporate or university network that is wholly or partially inaccessible to non-privileged users. In still other embodiments, communications network73may include one or more private networks with access to and/or from the Internet.

Communication network73may provide access to computers72. User computers72may be computers utilized by users70or other customers of data center85. For instance, user computer72aor72bmay be a server, a desktop or laptop personal computer, a tablet computer, a wireless telephone, a personal digital assistant (PDA), an e-book reader, a game console, a set-top box or any other computing device capable of accessing data center85. User computer72aor72bmay connect directly to the Internet (e.g., via a cable modem or a Digital Subscriber Line (DSL)). Although only two user computers72aand72bare depicted, it should be appreciated that there may be multiple user computers.

User computers72may also be utilized to configure aspects of the computing resources provided by data center85. In this regard, data center85might provide a gateway or web interface through which aspects of its operation may be configured through the use of a web browser application program executing on user computer72. Alternately, a stand-alone application program executing on user computer72might access an application programming interface (API) exposed by data center85for performing the configuration operations. Other mechanisms for configuring the operation of various web services available at data center85might also be utilized.

Servers76shown inFIG.8may be servers configured appropriately for providing the computing resources described above and may provide computing resources for executing one or more web services and/or applications. In one embodiment, the computing resources may be virtual machine instances78. In the example of virtual machine instances, each of the servers76may be configured to execute an instance manager80aor80b(which may be referred herein singularly as instance manager80or in the plural as instance managers80) capable of executing the virtual machine instances78. The instance managers80may be a virtual machine monitor (VMM) or another type of program configured to enable the execution of virtual machine instances78on server76, for example. As discussed above, each of the virtual machine instances78may be configured to execute all or a portion of an application.

In the example data center85shown inFIG.8, a router71may be utilized to interconnect the servers76aand76b. Router71may also be connected to gateway74, which is connected to communications network73. Router71may be connected to one or more load balancers, and alone or in combination may manage communications within networks in data center85, for example, by forwarding packets or other data communications as appropriate based on characteristics of such communications (e.g., header information including source and/or destination addresses, protocol identifiers, size, processing requirements, etc.) and/or the characteristics of the private network (e.g., routes based on network topology, etc.). It will be appreciated that, for the sake of simplicity, various aspects of the computing systems and other devices of this example are illustrated without showing certain conventional details. Additional computing systems and other devices may be interconnected in other embodiments and may be interconnected in different ways.

In the example data center85shown inFIG.8, a server manager75is also employed to at least in part direct various communications to, from and/or between servers76aand76b. WhileFIG.8depicts router71positioned between gateway74and server manager75, this is merely an exemplary configuration. In some cases, for example, server manager75may be positioned between gateway74and router71. Server manager75may, in some cases, examine portions of incoming communications from user computers72to determine one or more appropriate servers76to receive and/or process the incoming communications. Server manager75may determine appropriate servers to receive and/or process the incoming communications based on factors such as an identity, location or other attributes associated with user computers72, a nature of a task with which the communications are associated, a priority of a task with which the communications are associated, a duration of a task with which the communications are associated, a size and/or estimated resource usage of a task with which the communications are associated and many other factors. Server manager75may, for example, collect or otherwise have access to state information and other information associated with various tasks in order to, for example, assist in managing communications and other operations associated with such tasks.

It should also be appreciated that data center85described inFIG.8is merely illustrative and that other implementations might be utilized. It should also be appreciated that a server, gateway or other computing device may comprise any combination of hardware or software that can interact and perform the described types of functionality, including without limitation: desktop or other computers, database servers, network storage devices and other network devices, PDAs, tablets, cellphones, wireless phones, pagers, electronic organizers, Internet appliances, television-based systems (e.g., using set top boxes and/or personal/digital video recorders) and various other consumer products that include appropriate communication capabilities.

In at least some embodiments, a server that implements a portion or all of one or more of the technologies described herein may include a computer system that includes or is configured to access one or more computer-accessible media.FIG.9depicts a computer system that includes or is configured to access one or more computer-accessible media. In the illustrated embodiment, computing device15includes one or more processors10a,10band/or10n(which may be referred herein singularly as “a processor10” or in the plural as “the processors10”) coupled to a system memory20via an input/output (I/O) interface30. Computing device15further includes a network interface40coupled to I/O interface30.

In various embodiments, computing device15may be a uniprocessor system including one processor10or a multiprocessor system including several processors10(e.g., two, four, eight or another suitable number). Processors10may be any suitable processors capable of executing instructions. For example, in various embodiments, processors10may be embedded processors implementing any of a variety of instruction set architectures (ISAs), such as the x86, PowerPC, SPARC or MIPS ISAs or any other suitable ISA. In multiprocessor systems, each of processors10may commonly, but not necessarily, implement the same ISA.

System memory20may be configured to store instructions and data accessible by processor(s)10. In various embodiments, system memory20may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash®-type memory or any other type of memory. In the illustrated embodiment, program instructions and data implementing one or more desired functions, such as those methods, techniques and data described above, are shown stored within system memory20as code25and data26. Additionally, in this example, system memory20includes audio stream processing instructions27, which are instructions for executing any, or all, of the multi-talker audio stream separation, transcription and diarization techniques described above.

In one embodiment, I/O interface30may be configured to coordinate I/O traffic between processor10, system memory20and any peripherals in the device, including network interface40or other peripheral interfaces. In some embodiments, I/O interface30may perform any necessary protocol, timing or other data transformations to convert data signals from one component (e.g., system memory20) into a format suitable for use by another component (e.g., processor10). In some embodiments, I/O interface30may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard, for example. In some embodiments, the function of I/O interface30may be split into two or more separate components, such as a north bridge and a south bridge, for example. Also, in some embodiments some or all of the functionality of I/O interface30, such as an interface to system memory20, may be incorporated directly into processor10.

Network interface40may be configured to allow data to be exchanged between computing device15and other device or devices60attached to a network or networks50, such as other computer systems or devices, for example. In various embodiments, network interface40may support communication via any suitable wired or wireless general data networks, such as types of Ethernet networks, for example. Additionally, network interface40may support communication via telecommunications/telephony networks, such as analog voice networks or digital fiber communications networks, via storage area networks such as Fibre Channel SANs (storage area networks) or via any other suitable type of network and/or protocol.

In some embodiments, system memory20may be one embodiment of a computer-accessible medium configured to store program instructions and data as described above for implementing embodiments of the corresponding methods and apparatus. However, in other embodiments, program instructions and/or data may be received, sent or stored upon different types of computer-accessible media. Generally speaking, a computer-accessible medium may include non-transitory storage media or memory media, such as magnetic or optical media—e.g., disk or DVD/CD coupled to computing device15via I/O interface30. A non-transitory computer-accessible storage medium may also include any volatile or non-volatile media, such as RAM (e.g., SDRAM, DDR SDRAM, RDRAM, SRAM, etc.), ROM (read only memory) etc., that may be included in some embodiments of computing device15as system memory20or another type of memory. Further, a computer-accessible medium may include transmission media or signals such as electrical, electromagnetic or digital signals conveyed via a communication medium, such as a network and/or a wireless link, such as those that may be implemented via network interface40.

A compute node, which may be referred to also as a computing node, may be implemented on a wide variety of computing environments, such as commodity-hardware computers, virtual machines, web services, computing clusters and computing appliances. Any of these computing devices or environments may, for convenience, be described as compute nodes.

As set forth above, content may be provided by a content provider to one or more clients. The term content, as used herein, refers to any presentable information, and the term content item, as used herein, refers to any collection of any such presentable information. A content provider may, for example, provide one or more content providing services for providing content to clients. The content providing services may reside on one or more servers. The content providing services may be scalable to meet the demands of one or more customers and may increase or decrease in capability based on the number and type of incoming client requests. Portions of content providing services may also be migrated to be placed in positions of reduced latency with requesting clients. For example, the content provider may determine an “edge” of a system or network associated with content providing services that is physically and/or logically closest to a particular client. The content provider may then, for example, “spin-up,” migrate resources or otherwise employ components associated with the determined edge for interacting with the particular client. Such an edge determination process may, in some cases, provide an efficient technique for identifying and employing components that are well suited to interact with a particular client, and may, in some embodiments, reduce the latency for communications between a content provider and one or more clients.

In addition, certain methods or process blocks may be omitted in some implementations. The methods and processes described herein are also not limited to any particular sequence, and the blocks or states relating thereto can be performed in other sequences that are appropriate. For example, described blocks or states may be performed in an order other than that specifically disclosed, or multiple blocks or states may be combined in a single block or state. The example blocks or states may be performed in serial, in parallel or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments.