Patent Publication Number: US-2023154468-A1

Title: Hypothesis stitcher for speech recognition of long-form audio

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation application of and claims priority to U.S. patent application Ser. No. 17/127,938, entitled “HYPOTHESIS STITCHER FOR SPEECH RECOGNITION OF LONG-FORM AUDIO,” filed on Dec. 18, 2020, the disclosure of which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     End-to-end (E2E) automatic speech recognition (ASR) systems use a single neural network (NN) to transduce audio to word sequences, and are thus typically simpler than earlier ASR systems. E2E ASR solutions typically intake short audio segments to process a full utterance prior to producing a hypothesis. Unfortunately, models trained on short utterances generally underperform when applied to speech that exceeds the training data length. Such scenarios may occur with long-form speech (e.g., speech lasting 10 minutes or more), which may be encountered when transcribing streaming audio and in other ASR tasks. 
     SUMMARY 
     The disclosed examples are described in detail below with reference to the accompanying drawing figures listed below. The following summary is provided to illustrate some examples disclosed herein. It is not meant, however, to limit all examples to any particular configuration or sequence of operations. 
     A hypothesis stitcher for speech recognition of long-form audio provides superior performance, such as higher accuracy and reduced computational cost. An example disclosed operation includes: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within each of the plurality of audio segments; performing automatic speech recognition (ASR) on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merging at least a portion of the short-segment hypotheses into a first merged hypothesis set; inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a window change (WC) symbol; and consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. Multiple variations are disclosed, including alignment-based stitchers and serialized stitchers, which may operate as speaker-specific stitchers or multi-speaker stitchers, and may further support multiple options for differing hypothesis configurations. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The disclosed examples are described in detail below with reference to the accompanying drawing figures listed below: 
         FIG.  1    illustrates an arrangement for speech recognition that advantageously employs a hypothesis stitcher for speech recognition of long-form audio; 
         FIGS.  2 A and  2 B  illustrates examples of window overlap, as may be used with the arrangement of  FIG.  1   ; 
         FIG.  3    illustrates further details for examples of the hypothesis stitcher of  FIG.  1   ; 
         FIG.  4    illustrates example hypothesis sets for an alignment-based stitcher, as may be used in the arrangement of  FIG.  1   ; 
         FIG.  5    illustrates examples of merged hypothesis sets for a serialized stitcher, as may be used in the arrangement of  FIG.  1   ; 
         FIGS.  6 A,  6 B,  6 C, and  6 D  illustrate examples of merged hypothesis sets for multi-speaker serialized stitcher variations, as may be used in the arrangement of  FIG.  1   ; 
         FIG.  7    is a flowchart illustrating exemplary operations associated with the arrangement of  FIG.  1   ; 
         FIG.  8    is another flowchart illustrating exemplary operations associated with the arrangement of  FIG.  1   ; and 
         FIG.  9    is a block diagram of an example computing environment suitable for implementing some of the various examples disclosed herein. 
     
    
    
     Corresponding reference characters indicate corresponding parts throughout the drawings. 
     DETAILED DESCRIPTION 
     The various examples will be described in detail with reference to the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts. References made throughout this disclosure relating to specific examples and implementations are provided solely for illustrative purposes but, unless indicated to the contrary, are not meant to limit all examples. 
     A hypothesis stitcher for speech recognition of long-form audio provides superior performance, such as higher accuracy and reduced computational cost. An example disclosed operation includes: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within each of the plurality of audio segments; performing automatic speech recognition (ASR) on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merging at least a portion of the short-segment hypotheses into a first merged hypothesis set; inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a window change (WC) symbol; and consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. Multiple variations are disclosed, including alignment-based stitchers and serialized stitchers, which may operate as speaker-specific stitchers or multi- speaker stitchers, and may further support multiple options for differing hypothesis configurations. 
     Aspects of the disclosure improve the speed and accuracy of speech recognition by merging short-segment hypotheses into a merged hypothesis set and consolidating, with a network-based hypothesis stitcher, a merged hypothesis set into a consolidated hypothesis. The network-based hypothesis stitcher provides superior accuracy. Some examples employ a serialized stitcher that does not require alignment of odd and even hypothesis sequences (e.g., word alignment), reducing the required degree of overlap, and thus cutting computational cost. 
     The hypothesis stitcher intakes multiple hypotheses from short-segmented audio and outputs a fused single hypothesis, significantly improving speaker-attributed word error rate (SA-WER) for long-form multi-speaker audio. As used herein, a hypothesis is an estimated content of audio, and may include a sequence of estimated words or tokens representing words. A hypothesis may further contain other estimated content such as speaker identification, language identification, and other speaker characterizations, along with other tags or symbols. Multiple variants of model architectures are disclosed, including some that have reduced computational cost, due to the relaxation of overlap requirements. 
     Examples segment long audio using sliding window with overlaps among the segments, end-to-end (E2E) ASR is applied to each window to generate hypotheses. The hypotheses from each window are fused into a single hypothesis using a sequence-to-sequence model that has been trained to fuse multiple hypotheses from overlapping windows. By using a machine learning (ML) module, the hypotheses fusion can be executed with a significantly high accuracy. 
       FIG.  1    illustrates an arrangement  100  for speech recognition that advantageously employs a hypothesis stitcher for speech recognition of long-form audio. An audio stream  102  is received (captured) by a microphone  104  from a plurality of speakers  106  that includes a speaker  106   a , a speaker  106   b , and a speaker  106   c . Audio stream  102  is received and segmented by audio segmenter  108  into a plurality of audio segments  110 . As shown, plurality of audio segments  110  includes an audio segment  111 , an audio segment  112 , an audio segment  113 , an audio segment  114 , and an audio segment  115 , although it should be understood that a different number of audio segments may be used. 
     Turning briefly to  FIGS.  2 A and  2 B , overlap of audio segments will be described.  FIG.  2 A  illustrates 50% window overlap in overlap scenario  200   a , in which each of the odd-numbered audio segments stop and start back-to-back (e.g., without gaps), and the even-numbered audio segments similarly stop and start back-to-back (e.g., without gaps). The changes between odd-numbered windows occur within the even-numbered windows, and the changes between even-numbered windows similarly occur within the odd-numbered windows, producing parallel, staggered sequences. Audio stream  102  is shown twice, once annotated with odd-numbered audio segments (e.g., audio segments  111 ,  113 , and  115 ) and also annotated with even-numbered audio segments (e.g., audio segments  112  and  112 ). Window length  211  for audio segment  111  is consistent for other audio segments  112 - 115 , for example, matching the window length  212  for audio segment  112 . An overlap duration  202  is half (50%) of each window length  211  and window length  212 . Together, the five audio segments  111 - 115  cover a time period  204 . In some examples, window length  211  is 16 seconds, or 30 seconds, or some other time period having a duration of less than a minute, whereas the length of audio stream may be in excess of 10 minutes or even in excess of an hour. 
       FIG.  2 B  illustrates 25% window overlap in overlap scenario  200   b . The changes between odd-numbered windows occur within the even-numbered windows, and the changes between even-numbered windows similarly occur within the odd-numbered windows, producing parallel, staggered sequences—but allowing for gaps within each of the odd and even sequences. Audio stream  102  is shown twice, once annotated with odd-numbered audio segments (e.g., audio segments  111 ,  113 , and  115 ) and also annotated with even-numbered audio segments (e.g., audio segments  112  and  112 ). Window length  211  for audio segment  111  is consistent for other audio segments  112 - 115 , for example, matching the window length  212  for audio segment  112 . An overlap duration  206  is one-fourth (25%) of each window length  211  and window length  212 . Together, the five audio segments  111 - 115  cover a time period  208 , which is longer than time period  204  for overlap scenario  200   a  (having 50% window overlap). 
     Thus, for the same length of time as time period  204 , fewer audio segments are processed for 25% overlap than for 50% overlay, reducing computational cost. The overlap of hypotheses follows the overlap of audio segments. It should be understood that aspects of the disclosure may use differing amounts of overlap, including overlap as low as 10% or lower. 
     Returning to  FIG.  1   , plurality of audio segments  110  is provided to speaker identification  120 , which uses speaker profiles  122  (e.g., information regarding speaker characteristics of speakers  106   a - 106   c ) to associate each of speakers  106   a - 106   c  with utterances within plurality of audio segments  110 . Plurality of audio segments  110  is also provided to E2E speech recognition engine (SRE)  130  to perform speech recognition and output hypotheses. In some examples, a different form of SRE, rather than E2E, may be employed. E2E SRE  130  is trained by E2E trainer  132 , which uses speech recognition training data  134 . Hypotheses output by E2E SRE  130  are grouped according to speaker, by speaker grouping  136 , into speaker-specific short-segment hypotheses  138 . ASR is performed on each of audio segments  111 - 115  to produce {Ŷ m,1 , . . . Ŷ m,K }, where K is the number of speakers  106   a - 106   c  (and is also the number of profiles within speaker profiles  122  for this example). 
     Short-segment hypotheses  138  is illustrated with an example set of fifteen hypotheses, six for each of speakers  106   a - 106   c . S 1 H 1 , S 1 H 2 , S 1 H 3 , S 1 H 4 , S 1 H 5 , and S 1 H 6  are six hypotheses for speaker  106   a , in order of occurrence. S 2 H 1 , S 2 H 2 , S 2 H 3 , S 2 H 4 , S 2 H 5 , and S 2 H 6  are six hypotheses for speaker  106   b , in order of occurrence. S 3 H 1 , S 3 H 2 , S 3 H 3 , S 3 H 4 , S 3 H 5 , and S 3 H 6  are six hypotheses for speaker  106   c , in order of occurrence. It should be understood that three speakers with six utterances each (producing the six hypotheses each) is only an example. 
     A concatenator  140  merges at least a portion of (e.g., at least some of) short-segment hypotheses  138  into merged hypotheses  150 , which includes at least merged hypothesis set  150   a . In general, the hypotheses of short-segment hypotheses  138  may be merged into sets and take on the form shown in Equation 1: 
         Ŷ   k   ={Ŷ   1,k   , . . . Ŷ   M,k }  Eq. (1)
 
     where Ŷ m,k  represents the hypothesis for speaker k in audio segment m, k ranges from 1 to the number of speakers K, and m ranges from 1 to the number of audio segments M. For example, audio stream  102  is segmented into M segments (with overlaps). Ŷ k  is the merged hypothesis set (e.g., merged hypothesis set  150   a ) for speaker k. In some examples, if a speaker k is not detected in an audio segment m, the hypothesis Ŷ m,k  is set to an empty sequence. 
     Multiple options are disclosed in the following figures for the operation of concatenator  140  and the format of merged hypotheses  150 . Variations include whether merged hypotheses  150  includes speaker-specific merged hypothesis sets (e.g., each of merged hypothesis set  150   a , merged hypothesis set  150   b , and merged hypothesis set  150   a  is for only a single one if speakers  106   a - 106   c ) or whether merged hypotheses  150  includes a multi-speaker version of merged hypothesis set  150   a.    
     A symbol inserter  142  inserts stitching symbols  144  into merged hypotheses  150 , for example, into merged hypothesis set  150   a  and also merged hypothesis sets  150   b  and  150   c , if they are used. Stitching symbols  144  include a generic window change (WC) symbol  144   a , and in some examples, include an even window change (WCE) symbol  144   b  (indicating a change from an odd-numbered window to an even-numbered window) and an odd window change (WCO) symbol  144   c  (changing from an even-numbered window to an odd-numbered window). Window changes correspond to the end of a word or token sequence recognized from one audio segment to the start of a word or token sequence recognized from the next audio segment. In some examples, stitching symbols  144  may further include: a speaker identification (SPKR_k, where k is an index number for the speaker), speaker characteristics (e.g., language (LANG_k), speaker age (AGE_k), and accent (ACCENT_k)), and hypotheses ranks. Multiple options are disclosed for using stitching symbols  144 , as shown in  FIGS.  4 - 6 D . 
     A hypothesis stitcher  300  consolidates merged hypotheses  150  into consolidated hypotheses  160 . In some examples, this may be accomplished by merging speaker-specific merged hypothesis sets  150   a - 150   c  into speaker-specific consolidated hypotheses (e.g., consolidated hypothesis  160   a , consolidated hypothesis  160   b , and consolidated hypothesis  160   c ), whereas in some other examples, merged hypothesis set  150   a  is a multi-speaker merged hypothesis set that is merged into a multi-speaker version of consolidated hypothesis  160   a . Hypothesis stitcher  300  may be network-based, and in some examples may comprise a neural network (NN). Various configurations are disclosed, for example, an alignment-based stitcher and a serialized stitcher that does not use an alignment of odd and even hypothesis sequences, and thus may have a relaxed overlap requirement. Further detail regarding hypothesis stitcher  300 , a stitcher trainer  310 , and hypothesis stitcher training data  312  is provided in relation to  FIG.  3   . 
     Consolidated hypothesis  160   a  is output as a transcription  170 , which may be used for various tasks for which ASR results are useful, including live transcription of a conversation (e.g., a video call or speech) or streaming video, and voice commands. In some examples, consolidated hypothesis  160   a  is a multi-speaker consolidated hypothesis, and includes the multiple speakers (e.g., speakers  106   a - 106   c ). In some examples, each of consolidated hypothesis  160   a - 160   c  is a speaker-specific consolidated hypothesis and transcription  170  will then be a speaker-specific transcription, unless consolidated hypothesis  160   a - 160   c  are merged into a multi-speaker version of transcription  170  by a transcription merger and annotator  162 . In some examples, transcription merger and annotator  162  intakes each of consolidated hypothesis  160   a - 160  and outputs a multi-speaker version of transcription  170 . In some examples, transcription merger and annotator  162  annotates transcription  170  with timestamps, obtained by a timer  164 , which may be used for defining time windows used by audio segmenter  108  (so that the timestamps are properly synchronized with audio stream  102 ). 
       FIG.  3    illustrates further details for hypothesis stitcher  300 . Hypothesis stitcher  300  comprises an encoder  304 , a decoder  306 , and an embedding function  302 . Embedding function  302  intakes a symbol (e.g., a word, a token, or a stitching symbol) and outputs a vector (called an embedding) corresponding to the symbol. In some examples, hypothesis stitcher  300  comprises a transformer-based attention encoder decoder architecture. In some examples, hypothesis stitcher  300  comprises a sequence-to-sequence model (e.g., a trained NN) that merges multiple hypotheses from short-segmented audio into a single hypothesis. In some examples, hypothesis stitcher  300  is trained using conversations that have been segmented according to the overlap that will be used during operations, tagged with stitching symbols  144  (so that hypothesis stitcher  300  learns stitching symbols  144 ), and labeled for training. That is, hypothesis stitcher training data  312  includes stitching symbols  144  and has overlaps similar to overlaps in merged hypothesis set  150   a.    
     In some examples, errors  314  are inserted into hypothesis stitcher training data  312  so that hypothesis stitcher  300  learns to correct errors, such as incorrect word  316  (e.g., mistakenly-recognized words in hypotheses) and incorrect speaker  318  (e.g., a mis-identified speaker). Variations such as an alignment-based stitcher and serialized stitcher are described in further detail in relation to  FIGS.  4  and  5   . 
       FIG.  4    illustrates example hypothesis sequences, sequence  400   o  (o for “odd”) and sequence  400   e  (e for “even”), which are combined into hypothesis set  150   a  for an alignment-based stitcher version of hypothesis stitcher  300 . In the scenario depicted, 50% overlap is used, in which the odd-numbered audio segments stop and start, and overlap with the even-numbered audio segments as indicated in  FIG.  2 A . The hypotheses from odd-numbered (e.g.,  401 ) and even-numbered (e.g.,  402 ) hypotheses sets are jointed into two sequences  400   o  and  400   e , as indicated in  FIG.  4   . 
     Sequence  400   o  (Ŷ o,wc   k ) has an odd numbered (1 is odd) hypothesis  401 , and other odd numbered hypotheses, for speaker k. Sequence  400   e  (Ŷ e,wc   k ) has an even numbered (2 is even) hypothesis  40 , and other even numbered hypotheses, also for speaker k. WC symbol  144   a  is inserted between the hypotheses to indicate a change of windows corresponding to changing from one audio segment to the next. Sequences  400   o  and  400   e  are word-aligned as a sequence of word pairs &lt;o 1 , e 1 &gt;, &lt;o 2 , e 2 &gt;, . . . &lt;o L , e L &gt; for L pairs, where WC may be o l  or e l  for some pair l. Sequences  400   o  and  400   e  are then fused into merged hypothesis set  150   a  (or another merged hypothesis set) for consolidation by hypothesis stitcher  300 . 
       FIG.  5    illustrates two examples of hypothesis set  150   a  for a serialized stitcher version of hypothesis stitcher  300  are shown alternatively as hypothesis set  510  or hypothesis set  520 . In this scenario, overlap may be less than 50% because word alignment is not used. In some examples, the hypotheses for speaker k are joined as shown for hypothesis set  510 , which has both odd and even numbered hypotheses, for example odd-numbered hypothesis  501  (Ŷ 1,k  ), even-numbered hypothesis  502  (Ŷ 2,k  ), and WC symbol  144   a  inserted between hypotheses. Hypothesis set  520  is similar, although using WC symbols designated as even and odd, for example WCE symbol  144   b  and WCO symbol  144   c . Hypothesis set  510  or  520  is provided as merged hypothesis set  150   a  (or another merged hypothesis set) for consolidation by hypothesis stitcher  300 . 
       FIGS.  6 A- 6 D  illustrate multi-speaker data sets, in which all speakers&#39; hypotheses are concatenated into a single sequence, and using a SPKR symbol as one of stitching symbols  144 . In some examples, the SPRK symbol indicates a speaker number k, as SPKR_k. To enable the relatively long sequences to be easily shown in  FIGS.  6 A- 6 D , sequences uses W 1 , W 2 , . . . to indicate hypotheses for words in merged hypotheses  150  (e.g., (Ŷ m,k  ) and SPKR_k is abbreviated as S 1 , S 2 , S 3 , S 4 , and S 5 , for k=1, 2, 3, 4, 5, respectively. That is, S 1  is the abbreviated SPKR_k symbol for the first speaker (k=1). 
     Two variations for ordering SPKR symbols are shown in  FIG.  6 A . Sequence  602  is ordered according to the order of a speaker&#39;s first-time appearance in audio stream  102  (and transcription  170 ). Sequence  604  shows another possible variation, ordered according to the order of a speaker&#39;s profile in speaker profiles  122 . It should be noted that, because of overlap in the windows (e.g., audio segments  111 - 115 ) some of the hypotheses in subsequent windows (e.g., words preceding a WC, WCE, or WCO symbol) correspond. That is, W 11  and W 12  may correspond to W 9  and W 10 , for example. The output of hypothesis stitcher  300  is shown as sequence  606 , in which W 1 ′, W 2 ′, . . . indicate the words selected by hypothesis stitcher  300  for consolidated hypothesis  160   a  (or another consolidated hypothesis). In some examples, speaker symbols are assigned for along with other stitching symbols (e.g., &lt;WCO&gt; and &lt;WCE&gt;), and may take the form of a special identification, such as &lt;Sn&gt;, where n indicated the speaker number (according to an index in speaker profiles  122 ). 
       FIG.  6 B  shows variations for using SPKR symbols in the output of hypothesis stitcher  300  (e.g., consolidated hypothesis  160   a , or another consolidated hypothesis) as stitching symbols. Sequence  612  shows the same sequence of words (or tokens) as sequence  602  of  FIG.  6 A , but with each word annotated. A special SPKR symbol &lt;S 0 &gt; may be assigned to each stitching symbol. The output of hypothesis stitcher  300  may alternatively be in the format of sequence  614 , in which each word (or token) is annotated with a SPKR symbol, or in the format of sequence  616 , in which the SPKR symbol is used at the end of a sequence of words attributed to a particular speaker (when the following word is attributed to a different speaker. In the illustrated example, sequences  606  and  616  are similar. 
       FIG.  6 C  shows variations for using LANG symbols in the output of hypothesis stitcher  300  (e.g., consolidated hypothesis  160   a , or another consolidated hypothesis) as stitching symbols. Sequence  622  is similar to sequence  612  of  FIG.  6 B , but adds a language annotation after each word. The output of hypothesis stitcher  300  may alternatively be in the format of sequence  624 , in which each word (or token) is annotated with a LANG symbol, or in the format of sequence  626 , in which the LANG symbol is used at the end of a sequence of words attributed to a particular speaker or a change in the detected language. In the format of sequence  622 , a special LANG symbol &lt;L 0 &gt; may be assigned to each stitching symbol. Other speaker attributes (speaker age, accent, and others) may also be embedded. When age annotation is included with stitching symbols, the annotation may take the form of a symbol that incorporates the actual numerical value estimated for a speaker. 
       FIG.  6 D  shows the use of multiple ranked hypotheses, for example the N-best hypotheses, as estimated by E2E SRE  130 . In some examples, E2E SRE  130  is able to output more than a single hypothesis per detected word, and outputs multiple ranked hypotheses (ranked by probability of being correct). Sequence  632  shows a single speaker scenario in which a set of four words is identified as a best guess (N 1 ) as W 1 , W 2 , W 3 , and W 4 , a second best guess (N 2 ) as W 1 *, W 2 *, W 3 *, and W 4 *, and a third best guess (N 3 ) as W 1 **, W 2 **, W 3 **, and W 4 **. An alternative sequence  634  shows the use of a best guess symbol N* in place of specific guess rank values, in which rank is inferred by the order. N 1 , N 2 , N 3 , and/or N* are used as stitching symbols in the input to hypothesis stitcher  300 . The output of hypothesis stitcher  300  is shown as sequence  636 , with selected words W 1 ′, W 2 ′, W 3 ′, and W 4 ′. A multi-speaker version uses sequence  638 , in which the speaker is annotated after each set of N-best guesses. The output of hypothesis stitcher  300  is shown as sequence  640  with selected words W 1 ′, W 2 ′, W 3 ′, W 4 ′, W 5 ′, W 6 ′, W 7 ′, W 8 ′, W 9 ′, and W 10 ′. 
       FIG.  7    is a flowchart  700  illustrating exemplary operations involved in performing speech recognition. In some examples, operations described for flowchart  700  are performed by computing device  900  of  FIG.  9   . Flowchart  700  commences with operation  702 , which includes training E2E SRE  130 . In some examples, a joint model SRE (e.g., an end-to-end speaker-attributed ASR model) and speaker identification is trained in operation  702 , and operations  712 ,  714 ,  716  below use this joint model. Operation  704  includes training hypothesis stitcher  300  with hypothesis stitcher training data  312  comprising stitching symbols. In some examples, hypothesis stitcher training data  312  has overlaps similar to overlaps in merged hypothesis set  150   a . Operation  706 , which is included in operation  704 , includes inserting errors  314  into hypothesis stitcher training data  312 . In some examples, inserted errors  314  include an incorrectly identified word (in an overlapped region of a segment) or an incorrect speaker identification. In some examples, hypothesis stitcher  300  comprises an encoder and a decoder. In some examples, hypothesis stitcher  300  comprises a neural network. In some examples, hypothesis stitcher  300  comprises a transformer-based attention encoder decoder architecture. In some examples, hypothesis stitcher  300  comprises an alignment-based stitcher. In some examples, hypothesis stitcher  300  comprises a serialized stitcher that does not use an alignment of odd and even hypothesis sequences. In some examples, hypothesis stitcher  300  uses 25% overlap or less 
     Operation  708  includes receiving audio stream  102 . Operation  710  includes segmenting audio stream  102  into plurality of audio segments  110 . In some examples, each of the audio segments have a duration of less than a minute. Operation  712  includes identifying plurality of speakers  106  (e.g., identifying each of speakers  106   a - 106   c ) within each of the plurality of audio segments  110  (e.g., within audio stream  102 ). Operation  714  includes determining speaker characteristics. In some examples, the speaker characteristics are selected from a list consisting of: language, speaker age, and accent. Operation  716  includes performing ASR on each of plurality of audio segments  110  (e.g., audio segments  111 - 115 ) to generate plurality of short-segment hypotheses  138 . In some examples, performing ASR comprises performing E2E ASR. In some example, operations  712 - 716  are performed as a single operation, using a common joint model (e.g., the end-to-end speaker-attributed ASR model described above). In some examples, short-segment hypotheses  138  comprise tokens representing words. 
     In some examples, short-segment hypotheses  138  are specific to a speaker, and so following operations  718 - 728  are performed for each speaker, using speaker-specific data sets (e.g., short-segment hypotheses  138 , merged hypotheses  156 , and consolidated hypotheses  160 ). In some examples, short-segment hypotheses  138  are for multiple speakers, and so following operations  718 - 728  are performed using multi-speaker data sets (e.g., multi-speaker versions of short-segment hypothesis  138 , merged hypothesis set  156   a , and consolidated hypothesis  160   a ). 
     Operation  718  includes merging at least a portion of short-segment hypotheses  138  into merged hypothesis set  150   a , and is performed using operations  720 - 724 . In some examples, merging at least a portion of short-segment hypotheses  138  into merged hypothesis set  150   a  comprises concatenated tokenized hypotheses. In some examples, merged hypothesis set  150   a  comprises a multi-speaker merged hypothesis set. In some examples, merged hypothesis set  150   a  comprises hypotheses ranks. In some examples, merged hypothesis set  150   a  is specific to a first speaker of plurality of speakers  106 , and so operation  718  further includes merging at least a portion of short-segment hypotheses  138  into merged hypothesis set  150   b  specific to a second speaker of plurality of speakers  106 . Operation  720  includes grouping separate ASR results by speaker. 
     Operation  722  includes inserting stitching symbols  144  into merged hypothesis set  150   a , stitching symbols  144  including a WC symbol (e.g., WC symbol  144   a , WCE symbol  144   b , and/or WCO symbol  144   c ). In some examples, operation  722  further includes, based on at least the determined speaker characteristics inserting speaker characteristic tags as stitching symbols  144  into merged hypothesis set  150   a . In some examples, stitching symbols  144  including at least one symbol selected from the list consisting of: a WC symbol, a WCE symbol, a WCO symbol, an SPKR symbol, an SPKR k symbol, and a speaker characteristic symbol or value. 
     In speaker-specific scenarios, operation  722  further includes inserting stitching symbols into merged hypothesis set  150   b . In some examples, stitching symbols  144  further include a speaker identification (e.g., SPKR_k). In examples using an alignment-based stitcher (see  FIG.  4   ), merged hypothesis set  150   a  comprises an odd hypothesis sequence and an even hypothesis sequence, and operation  724  includes aligning the odd hypothesis sequence with the even hypothesis sequence. In some examples, aligning the odd hypothesis sequence with the even hypothesis sequence comprises pairing words or tokens in the odd sequence with words or tokens in the even sequence. 
     Operation  726  includes  726  consolidating, with (network-based) hypothesis stitcher  300 , merged hypothesis set  150   a  into consolidated hypothesis  160   a . In some examples, consolidated hypothesis  160   a  comprise tokens representing words. In some examples, consolidated hypothesis  160   a  is specific to a speaker (e.g., one of speakers  106   a - 106   c . In the situation of speaker-specific consolidated hypotheses  160 , operation  726  also includes consolidating, with hypothesis stitcher  300 , merged hypothesis set  150   b  into consolidated hypothesis  160   b , specific to a second speaker. 
     Operation  728  includes outputting consolidated hypothesis  160   a  as transcription  170 . If consolidated hypothesis  160   a  had been a speaker-specific consolidated hypothesis, operations  730 - 734  are used to produce a multi-speaker version of transcription  170 . However, if operation  728  had output multi-speaker version of transcription  170 , operation  730 - 734  may be unnecessary. Operation  730  includes merging consolidated hypothesis  160   a  with consolidated hypothesis  160   b  to generate a multi-speaker version of transcription  170 . Operation  732  includes identifying speakers  106   a - 106   c  within the multi-speaker version of transcription  170 . Operation  734  includes outputting the multi-speaker version of transcription  170 , if operation  728  had only output a speaker-specific version of transcription  170 . 
       FIG.  8    is a flowchart  800  that illustrates exemplary operations involved in performing speech recognition. In some examples, operations described for flowchart  800  are performed by computing device  900  of  FIG.  9   . Flowchart  800  commences with operation  802 , which includes segmenting the audio stream into a plurality of audio segments. Operation  804  includes identifying a plurality of speakers within the audio stream (e.g., within the plurality of audio segments). Operation  806  includes performing ASR on each of the plurality of audio segments to generate a plurality of short-segment hypotheses. Operation  808  includes merging at least a portion of the short-segment hypotheses into a first merged hypothesis set. Operation  810  includes inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a WC symbol. Operation  812  includes consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. 
     Additional Examples 
     An example method of speech recognition comprises: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within the audio stream; performing ASR on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merging at least a portion of the short-segment hypotheses into a first merged hypothesis set; inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a WC symbol; and consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. 
     An example system for speech recognition comprises: a processor; and a computer-readable medium storing instructions that are operative upon execution by the processor to: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within the audio stream; perform ASR on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merge at least a portion of the short-segment hypotheses into a first merged hypothesis set; insert stitching symbols into the first merged hypothesis set, the stitching symbols including a WC symbol; and consolidate, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. 
     One or more example computer storage devices has computer-executable instructions stored thereon, which, on execution by a computer, cause the computer to perform operations comprising: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within the audio stream; performing ASR on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merging at least a portion of the short-segment hypotheses into a first merged hypothesis set; inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a window change (WC) symbol; and consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. 
     Alternatively, or in addition to the other examples described herein, examples may include any combination of the following:
         outputting the first consolidated hypothesis as a transcription;   the first merged hypothesis set is specific to a first speaker of the plurality of speakers;   the first consolidated hypothesis is specific to the first speaker;   merging at least a portion of the short-segment hypotheses into a second merged hypothesis set specific to a second speaker of the plurality of speakers;   inserting stitching symbols into the second merged hypothesis;   consolidating, with the hypothesis stitcher, the second merged hypothesis set into a second consolidated hypothesis specific to the second speaker;   the first merged hypothesis set comprises a multi-speaker merged hypothesis set;   the stitching symbols further include a speaker identification;   the hypothesis stitcher comprises an alignment-based stitcher;   the first merged hypothesis set comprises an odd hypothesis sequence and an even hypothesis sequence;   aligning the odd hypothesis sequence with the even hypothesis sequence;   the hypothesis stitcher comprises a serialized stitcher that does not use an alignment of odd and even hypothesis sequences;   the hypothesis stitcher uses 25% overlap or less;   the first merged hypothesis set comprises hypotheses ranks;   performing ASR comprises performing E2E ASR;   the short-segment hypotheses and the first consolidated hypothesis comprise tokens representing words;   aligning the odd hypothesis sequence with the even hypothesis sequence comprises pairing words or tokens in the odd sequence with words or tokens in the even sequence;   the hypothesis stitcher comprises an encoder and a decoder;   the hypothesis stitcher comprises a neural network;   the hypothesis stitcher comprises a transformer-based attention encoder decoder architecture.   determining speaker characteristics;   the speaker characteristics are selected from a list consisting of: language, speaker age, and accent;   based on at least the determined speaker characteristics inserting speaker characteristic tags as stitching symbols into the first merged hypothesis set;   the stitching symbols further including at least one symbol selected from the list consisting of: a WCE symbol, a WCO symbol, a SPKR symbol, a numbered speaker symbol, and a speaker characteristic symbol or value;   training the hypothesis stitcher with hypothesis stitcher training data comprising stitching symbols;   inserting errors into the hypothesis stitcher training data;   the inserted errors include an incorrectly identified word in an overlapped region of a segment or an incorrect speaker identification;   the hypothesis stitcher training data has overlaps similar to overlaps in the first merged hypothesis set;   merging at least a portion of the short-segment hypotheses into the first merged hypothesis set comprises concatenated tokenized hypotheses;   merging the first consolidated hypothesis with the second consolidated hypothesis to generate a multi-speaker transcription;   identifying speakers within the multi-speaker transcription;   outputting the multi-speaker transcription;   receiving the audio stream;   the audio segments have a duration of less than a minute; and   training the E2E SRE.       

     While the aspects of the disclosure have been described in terms of various examples with their associated operations, a person skilled in the art would appreciate that a combination of operations from any number of different examples is also within scope of the aspects of the disclosure. 
     Example Operating Environment 
       FIG.  9    is a block diagram of an example computing device  900  for implementing aspects disclosed herein, and is designated generally as computing device  900 . Computing device  900  is but one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the examples disclosed herein. Neither should computing device  900  be interpreted as having any dependency or requirement relating to any one or combination of components/modules illustrated. The examples disclosed herein may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program components, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program components including routines, programs, objects, components, data structures, and the like, refer to code that performs particular tasks, or implement particular abstract data types. The disclosed examples may be practiced in a variety of system configurations, including personal computers, laptops, smart phones, mobile tablets, hand-held devices, consumer electronics, specialty computing devices, etc. The disclosed examples may also be practiced in distributed computing environments when tasks are performed by remote-processing devices that are linked through a communications network. 
     Computing device  900  includes a bus  910  that directly or indirectly couples the following devices: computer-storage memory  912 , one or more processors  914 , one or more presentation components  916 , I/O ports  918 , I/O components  920 , a power supply  922 , and a network component  924 . While computing device  900  is depicted as a seemingly single device, multiple computing devices  900  may work together and share the depicted device resources. In one example embodiment, memory  912  is distributed across multiple devices, and processor(s)  914  is housed with different devices. 
     Bus  910  represents what may be one or more busses (such as an address bus, data bus, or a combination thereof). Although the various blocks of  FIG.  9    are shown with lines for the sake of clarity, delineating various components may be accomplished with alternative representations. For example, a presentation component such as a display device is an I/O component in some examples, and some examples of processors have their own memory. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of  FIG.  9    and the references herein to a “computing device.” Memory  912  may take the form of the computer-storage media references below and operatively provide storage of computer-readable instructions, data structures, program modules and other data for the computing device  900 . In some examples, memory  912  stores one or more of an operating system, a universal application platform, or other program modules and program data. Memory  912  is thus able to store and access data  912   a  and instructions  912   b  that are executable by processor  914  and configured to carry out the various operations disclosed herein. 
     In some examples, memory  912  includes computer-storage media in the form of volatile and/or nonvolatile memory, removable or non-removable memory, data disks in virtual environments, or a combination thereof. Memory  912  may include any quantity of memory associated with or accessible by the computing device  900 . Memory  912  may be internal to the computing device  900  (as shown in  FIG.  9   ), external to the computing device  900  (not shown), or both (not shown). Examples of memory  912  in include, without limitation, random access memory (RAM); read only memory (ROM); electronically erasable programmable read only memory (EEPROM); flash memory or other memory technologies; CD-ROM, digital versatile disks (DVDs) or other optical or holographic media; magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices; memory wired into an analog computing device; or any other medium for encoding desired information and for access by the computing device  900 . Additionally, or alternatively, the memory  912  may be distributed across multiple computing devices  900 , for example, in a virtualized environment in which instruction processing is carried out on multiple devices  900 . For the purposes of this disclosure, “computer storage media,” “computer-storage memory,” “memory,” and “memory devices” are synonymous terms for the computer-storage memory  912 , and none of these terms include carrier waves or propagating signaling. 
     Processor(s)  914  may include any quantity of processing units that read data from various entities, such as memory  912  or I/O components  920 . Specifically, processor(s)  914  are programmed to execute computer-executable instructions for implementing aspects of the disclosure. The instructions may be performed by the processor, by multiple processors within the computing device  900 , or by a processor external to the client computing device  900 . In some examples, the processor(s)  914  are programmed to execute instructions such as those illustrated in the flow charts discussed below and depicted in the accompanying drawings. Moreover, in some examples, the processor(s)  914  represent an implementation of analog techniques to perform the operations described herein. In one example embodiment, the operations are performed by an analog client computing device  900  and/or a digital client computing device  900 . Presentation component(s)  916  present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. One skilled in the art will understand and appreciate that computer data may be presented in a number of ways, such as visually in a graphical user interface (GUI), audibly through speakers, wirelessly between computing devices  900 , across a wired connection, or in other ways. I/O ports  918  allow computing device  900  to be logically coupled to other devices including I/O components  920 , some of which may be built in. Example I/O components  920  include, for example but without limitation, a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc. 
     The computing device  900  may operate in a networked environment via the network component  924  using logical connections to one or more remote computers. In some examples, the network component  924  includes a network interface card and/or computer-executable instructions (e.g., a driver) for operating the network interface card. Communication between the computing device  900  and other devices may occur using any protocol or mechanism over any wired or wireless connection. In some examples, network component  924  is operable to communicate data over public, private, or hybrid (public and private) using a transfer protocol, between devices wirelessly using short range communication technologies (e.g., near-field communication (NFC), Bluetooth™ branded communications, or the like), or a combination thereof. Network component  924  communicates over wireless communication link  926  and/or a wired communication link  926   a  to a cloud resource  928  across network  930 . Various different examples of communication links  926  and  926   a  include a wireless connection, a wired connection, and/or a dedicated link, and in some examples, at least a portion is routed through the internet. 
     Although described in connection with an example computing device  900 , examples of the disclosure are capable of implementation with numerous other general-purpose or special-purpose computing system environments, configurations, or devices. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with aspects of the disclosure include, but are not limited to, smart phones, mobile tablets, mobile computing devices, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, gaming consoles, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile telephones, mobile computing and/or communication devices in wearable or accessory form factors (e.g., watches, glasses, headsets, or earphones), network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, virtual reality (VR) devices, augmented reality (AR) devices, mixed reality (MR) devices, holographic device, and the like. Such systems or devices may accept input from the user in any way, including from input devices such as a keyboard or pointing device, via gesture input, proximity input (such as by hovering), and/or via voice input. 
     Examples of the disclosure may be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices in software, firmware, hardware, or a combination thereof. The computer-executable instructions may be organized into one or more computer-executable components or modules. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. Aspects of the disclosure may be implemented with any number and organization of such components or modules. For example, aspects of the disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other examples of the disclosure may include different computer-executable instructions or components having more or less functionality than illustrated and described herein. In examples involving a general-purpose computer, aspects of the disclosure transform the general-purpose computer into a special-purpose computing device when configured to execute the instructions described herein. 
     By way of example and not limitation, computer readable media comprise computer storage media and communication media. Computer storage media include volatile and nonvolatile, removable and non-removable memory implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or the like. Computer storage media are tangible and mutually exclusive to communication media. Computer storage media are implemented in hardware and exclude carrier waves and propagated signals. Computer storage media for purposes of this disclosure are not signals per se. Exemplary computer storage media include hard disks, flash drives, solid-state memory, phase change random-access memory (PRAM), static random-access memory (SRAM), dynamic random-access memory (DRAM), other types of random-access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that may be used to store information for access by a computing device. In contrast, communication media typically embody computer readable instructions, data structures, program modules, or the like in a modulated data signal such as a carrier wave or other transport mechanism and include any information delivery media. 
     The order of execution or performance of the operations in examples of the disclosure illustrated and described herein is not essential, and may be performed in different sequential manners in various examples. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the disclosure. When introducing elements of aspects of the disclosure or the examples thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. The term “exemplary” is intended to mean “an example of.” The phrase “one or more of the following: A, B, and C” means “at least one of A and/or at least one of B and/or at least one of C.” 
     Having described aspects of the disclosure in detail, it will be apparent that modifications and variations are possible without departing from the scope of aspects of the disclosure as defined in the appended claims. As various changes could be made in the above constructions, products, and methods without departing from the scope of aspects of the disclosure, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.