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公开(公告)号：US10237413B2
公开(公告)日：2019-03-19
申请号：US15917425
申请日：2018-03-09
Applicant: Interactive Intelligence Group, Inc.
Inventor： Kevin O'Connor , Felix Immanuel Wyss
Abstract: A system and method are presented for the encoding of participants in a conference setting. In an embodiment, audio from conference participants in a voice-over-IP setting may be received and processed by the system. In an embodiment, audio may be received in a compressed form and de-compressed for processing. For each participant, return audio is generated, compressed (if applicable) and transmitted to the participant. The system may recognize when participants are using the same audio encoding format and are thus receiving audio that may be similar or identical. The audio may only be encoded once instead of for each participant. Thus, redundant encodings are recognized and eliminated resulting in less CPU usage.
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公开(公告)号：US10116793B2
公开(公告)日：2018-10-30
申请号：US15370120
申请日：2016-12-06
Applicant: Interactive Intelligence Group, Inc.
Inventor： Felix Immanuel Wyss , Matthew Alan Taylor , Kevin Charles Vlack
Abstract: A system and method are presented for learning call analysis. Audio fingerprinting may be employed to identify audio recordings that answer communications. In one embodiment, the system may generate a fingerprint of a candidate audio stream and compare it against known fingerprints within a database. The system may also search for a speech-like signal to determine if the endpoint contains a known audio recording. If a known audio recording is not encountered, a fingerprint may be computed for the contact and the communication routed to a human for handling. An indication may be made as to if the call is indeed an audio recording. The associated information may be saved and used for future identification purposes.
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公开(公告)号：US20180144040A1
公开(公告)日：2018-05-24
申请号：US15876050
申请日：2018-01-19
Applicant: INTERACTIVE INTELLIGENCE GROUP, INC.
Inventor： Kevin Vlack , Felix Immanuel Wyss
IPC: G06F17/30 , H03M7/30 , G10L19/018 , G10L25/54 , H04M3/51
CPC classification number: G06F16/284 , G06F16/683 , G10L19/018 , G10L25/54 , H03M7/3059 , H03M7/3066 , H03M7/3082 , H04M3/5158 , H04M2203/2027
Abstract: Systems and methods for the matching of datasets, such as input audio segments, with known datasets in a database are disclosed. In an illustrative embodiment, the use of the presently disclosed systems and methods is described in conjunction with recognizing known network message recordings encountered during an outbound telephone call. The methodologies include creation of a ternary fingerprint bitmap to make the comparison process more efficient. Also disclosed are automated methodologies for creating the database of known datasets from a larger collection of datasets.
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4.发明授权System and method for learning alternate pronunciations for speech recognition 有权用于学习语音识别的交替发音的系统和方法公开(公告)号：US09489943B2
公开(公告)日：2016-11-08
申请号：US14515607
申请日：2014-10-16
Applicant: Interactive Intelligence Group, Inc.
Inventor： Zhenhao Ge , Vivek Tyagi , Aravind Ganapathiraju , Ananth Nagaraja Iyer , Scott Allen Randal , Felix Immanuel Wyss
IPC: G10L15/187 , G06F17/28 , G09B19/04 , G10L15/06 , G09B19/06
CPC classification number: G10L15/063 , G06F17/2735 , G06F17/28 , G09B19/04 , G09B19/06 , G10L15/14 , G10L15/187 , G10L2015/081
Abstract: A system and method for learning alternate pronunciations for speech recognition is disclosed. Alternative name pronunciations may be covered, through pronunciation learning, that have not been previously covered in a general pronunciation dictionary. In an embodiment, the detection of phone-level and syllable-level mispronunciations in words and sentences may be based on acoustic models trained by Hidden Markov Models. Mispronunciations may be detected by comparing the likelihood of the potential state of the targeting pronunciation unit with a pre-determined threshold through a series of tests. It is also within the scope of an embodiment to detect accents.
Abstract translation: 公开了用于学习语音识别的替代发音的系统和方法。 通过发音学习可以覆盖另类名称发音，这些发音先前未被一般发音词典涵盖。 在一个实施例中，在单词和句子中检测电话级和音节级错误可以基于由隐马尔可夫模型训练的声学模型。 可以通过一系列测试来比较目标语音单元的潜在状态与预定阈值的可能性来检测微分。 检测重音也属于实施例的范围。
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公开(公告)号：US20150255075A1
公开(公告)日：2015-09-10
申请号：US14638198
申请日：2015-03-04
Applicant: Interactive Intelligence Group, Inc.
IPC: G10L19/005
CPC classification number: G10L19/005 , G10L15/02 , G10L15/08 , G10L15/14 , G10L15/20 , G10L25/18 , G10L25/21 , G10L2015/025 , G10L2019/0012
Abstract: A system and method are presented for the correction of packet loss in audio in automatic speech recognition (ASR) systems. Packet loss correction, as presented herein, occurs at the recognition stage without modifying any of the acoustic models generated during training. The behavior of the ASR engine in the absence of packet loss is thus not altered. To accomplish this, the actual input signal may be rectified, the recognition scores may be normalized to account for signal errors, and a best-estimate method using information from previous frames and acoustic models may be used to replace the noisy signal.
Abstract translation: 提出了一种用于在自动语音识别（ASR）系统中校正音频中的分组丢失的系统和方法。 如本文所示，分组丢失校正发生在识别阶段，而不修改在训练期间产生的任何声学模型。 因此，在没有丢包的情况下，ASR引擎的行为不会改变。 为了实现这一点，实际输入信号可以被纠正，识别分数可以被归一化以考虑信号误差，并且可以使用使用来自先前帧和声学模型的信息的最佳估计方法来代替噪声信号。
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Patent Agency Ranking
公开(公告)号：US10552457B2
公开(公告)日：2020-02-04
申请号：US15876050
申请日：2018-01-19
Applicant: INTERACTIVE INTELLIGENCE GROUP, INC.
Inventor： Kevin Vlack , Felix Immanuel Wyss
IPC: G10L19/018 , G06F16/28 , G06F16/683 , H03M7/30 , G10L25/54 , H04M3/51
Abstract: Systems and methods for the matching of datasets, such as input audio segments, with known datasets in a database are disclosed. In an illustrative embodiment, the use of the presently disclosed systems and methods is described in conjunction with recognizing known network message recordings encountered during an outbound telephone call. The methodologies include creation of a ternary fingerprint bitmap to make the comparison process more efficient. Also disclosed are automated methodologies for creating the database of known datasets from a larger collection of datasets.
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公开(公告)号：US10303800B2
公开(公告)日：2019-05-28
申请号：US14636474
申请日：2015-03-03
Applicant: Interactive Intelligence Group, Inc.
Inventor： Srinath Cheluvaraja , Ananth Nagaraja Iyer , Felix Immanuel Wyss
Abstract: A system and method are presented for optimization of audio fingerprint search. In an embodiment, the audio fingerprints are organized into a recursive tree with different branches containing fingerprint sets that are dissimilar to each other. The tree is constructed using a clustering algorithm based on a similarity measure. The similarity measure may comprise a Hamming distance for a binary fingerprint or a Euclidean distance for continuous valued fingerprints. In another embodiment, each fingerprint is stored at a plurality of resolutions and clustering is performed hierarchically. The recognition of an incoming fingerprint begins from the root of the tree and proceeds down its branches until a match or mismatch is declared. In yet another embodiment, a fingerprint definition is generalized to include more detailed audio information than in the previous definition.
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公开(公告)号：US10157620B2
公开(公告)日：2018-12-18
申请号：US14638198
申请日：2015-03-04
Applicant: Interactive Intelligence Group, Inc.
Abstract: A system and method are presented for the correction of packet loss in audio in automatic speech recognition (ASR) systems. Packet loss correction, as presented herein, occurs at the recognition stage without modifying any of the acoustic models generated during training. The behavior of the ASR engine in the absence of packet loss is thus not altered. To accomplish this, the actual input signal may be rectified, the recognition scores may be normalized to account for signal errors, and a best-estimate method using information from previous frames and acoustic models may be used to replace the noisy signal.
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公开(公告)号：US20180144739A1
公开(公告)日：2018-05-24
申请号：US15874612
申请日：2018-01-18
Applicant: INTERACTIVE INTELLIGENCE GROUP, INC.
Inventor： Yingyi Tan , Aravind Ganapathiraju , Felix Immanuel Wyss
IPC: G10L13/08
Abstract: A system and method are presented for the synthesis of speech from provided text. Particularly, the generation of parameters within the system is performed as a continuous approximation in order to mimic the natural flow of speech as opposed to a step-wise approximation of the feature stream. Provided text may be partitioned and parameters generated using a speech model. The generated parameters from the speech model may then be used in a post-processing step to obtain a new set of parameters for application in speech synthesis.
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10.发明申请公开(公告)号：US20180114525A1
公开(公告)日：2018-04-26
申请号：US15850106
申请日：2017-12-21
Applicant: INTERACTIVE INTELLIGENCE GROUP, INC.
Inventor： Vivek Tyagi , Aravind Ganapathiraju , Felix Immanuel Wyss
CPC classification number: G10L15/063 , G10L15/144 , G10L2015/025
Abstract: A system and method are presented for acoustic data selection of a particular quality for training the parameters of an acoustic model, such as a Hidden Markov Model and Gaussian Mixture Model, for example, in automatic speech recognition systems in the speech analytics field. A raw acoustic model may be trained using a given speech corpus and maximum likelihood criteria. A series of operations are performed, such as a forced Viterbi-alignment, calculations of likelihood scores, and phoneme recognition, for example, to form a subset corpus of training data. During the process, audio files of a quality that does not meet a criterion, such as poor quality audio files, may be automatically rejected from the corpus. The subset may then be used to train a new acoustic model.
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