Multi-Modal Speech Emotion Recognition Using Speech Embeddings and Audio Features
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Malware Classification with BERT
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family. -
Aviation Speech Recognition System Using Artificial Intelligence
SPEECH RECOGNITION SYSTEM OVERVIEW AVIATION SPEECH RECOGNITION SYSTEM USING ARTIFICIAL INTELLIGENCE Appareo’s embeddable AI model for air traffic control (ATC) transcription makes the company one of the world leaders in aviation artificial intelligence. TECHNOLOGY APPLICATION As featured in Appareo’s two flight apps for pilots, Stratus InsightTM and Stratus Horizon Pro, the custom speech recognition system ATC Transcription makes the company one of the world leaders in aviation artificial intelligence. ATC Transcription is a recurrent neural network that transcribes analog or digital aviation audio into text in near-real time. This in-house artificial intelligence, trained with Appareo’s proprietary dataset of flight-deck audio, takes the terabytes of training data and its accompanying transcriptions and reduces that content to a powerful 160 MB model that can be run inside the aircraft. BENEFITS OF ATC TRANSCRIPTION: • Provides improved situational awareness by identifying, capturing, and presenting ATC communications relevant to your aircraft’s operation for review or replay. • Provides the ability to stream transcribed speech to on-board (avionic) or off-board (iPad/ iPhone) display sources. • Provides a continuous representation of secondary audio channels (ATIS, AWOS, etc.) for review in textual form, allowing your attention to focus on a single channel. • ...and more. What makes ATC Transcription special is the context-specific training work that Appareo has done, using state-of-the-art artificial intelligence development techniques, to create an AI that understands the aviation industry vocabulary. Other natural language processing work is easily confused by the cadence, noise, and vocabulary of the aviation industry, yet Appareo’s groundbreaking work has overcome these barriers and brought functional artificial intelligence to the cockpit. -
A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech
A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible Speech Joshua Y. Kim1, Chunfeng Liu1, Rafael A. Calvo1*, Kathryn McCabe2, Silas C. R. Taylor3, Björn W. Schuller4, Kaihang Wu1 1 University of Sydney, Faculty of Engineering and Information Technologies 2 University of California, Davis, Psychiatry and Behavioral Sciences 3 University of New South Wales, Faculty of Medicine 4 Imperial College London, Department of Computing Abstract large-scale commercial products such as Google Home and Amazon Alexa. Mainstream ASR Automatic Speech Recognition (ASR) systems use only voice as inputs, but there is systems have proliferated over the recent potential benefit in using multi-modal data in order years to the point that free platforms such to improve accuracy [1]. Compared to machines, as YouTube now provide speech recognition services. Given the wide humans are highly skilled in utilizing such selection of ASR systems, we contribute to unstructured multi-modal information. For the field of automatic speech recognition example, a human speaker is attuned to nonverbal by comparing the relative performance of behavior signals and actively looks for these non- two sets of manual transcriptions and five verbal ‘hints’ that a listener understands the speech sets of automatic transcriptions (Google content, and if not, they adjust their speech Cloud, IBM Watson, Microsoft Azure, accordingly. Therefore, understanding nonverbal Trint, and YouTube) to help researchers to responses to unintelligible speech can both select accurate transcription services. In improve future ASR systems to mark uncertain addition, we identify nonverbal behaviors transcriptions, and also help to provide feedback so that are associated with unintelligible speech, as indicated by high word error that the speaker can improve his or her verbal rates. -
Learned in Speech Recognition: Contextual Acoustic Word Embeddings
LEARNED IN SPEECH RECOGNITION: CONTEXTUAL ACOUSTIC WORD EMBEDDINGS Shruti Palaskar∗, Vikas Raunak∗ and Florian Metze Carnegie Mellon University, Pittsburgh, PA, U.S.A. fspalaska j vraunak j fmetze [email protected] ABSTRACT model [10, 11, 12] trained for direct Acoustic-to-Word (A2W) speech recognition [13]. Using this model, we jointly learn to End-to-end acoustic-to-word speech recognition models have re- automatically segment and classify input speech into individual cently gained popularity because they are easy to train, scale well to words, hence getting rid of the problem of chunking or requiring large amounts of training data, and do not require a lexicon. In addi- pre-defined word boundaries. As our A2W model is trained at the tion, word models may also be easier to integrate with downstream utterance level, we show that we can not only learn acoustic word tasks such as spoken language understanding, because inference embeddings, but also learn them in the proper context of their con- (search) is much simplified compared to phoneme, character or any taining sentence. We also evaluate our contextual acoustic word other sort of sub-word units. In this paper, we describe methods embeddings on a spoken language understanding task, demonstrat- to construct contextual acoustic word embeddings directly from a ing that they can be useful in non-transcription downstream tasks. supervised sequence-to-sequence acoustic-to-word speech recog- Our main contributions in this paper are the following: nition model using the learned attention distribution. On a suite 1. We demonstrate the usability of attention not only for aligning of 16 standard sentence evaluation tasks, our embeddings show words to acoustic frames without any forced alignment but also for competitive performance against a word2vec model trained on the constructing Contextual Acoustic Word Embeddings (CAWE). -
Lecture 26 Word Embeddings and Recurrent Nets
CS447: Natural Language Processing http://courses.engr.illinois.edu/cs447 Where we’re at Lecture 25: Word Embeddings and neural LMs Lecture 26: Recurrent networks Lecture 26 Lecture 27: Sequence labeling and Seq2Seq Lecture 28: Review for the final exam Word Embeddings and Lecture 29: In-class final exam Recurrent Nets Julia Hockenmaier [email protected] 3324 Siebel Center CS447: Natural Language Processing (J. Hockenmaier) !2 What are neural nets? Simplest variant: single-layer feedforward net For binary Output unit: scalar y classification tasks: Single output unit Input layer: vector x Return 1 if y > 0.5 Recap Return 0 otherwise For multiclass Output layer: vector y classification tasks: K output units (a vector) Input layer: vector x Each output unit " yi = class i Return argmaxi(yi) CS447: Natural Language Processing (J. Hockenmaier) !3 CS447: Natural Language Processing (J. Hockenmaier) !4 Multi-layer feedforward networks Multiclass models: softmax(yi) We can generalize this to multi-layer feedforward nets Multiclass classification = predict one of K classes. Return the class i with the highest score: argmaxi(yi) Output layer: vector y In neural networks, this is typically done by using the softmax N Hidden layer: vector hn function, which maps real-valued vectors in R into a distribution … … … over the N outputs … … … … … …. For a vector z = (z0…zK): P(i) = softmax(zi) = exp(zi) ∕ ∑k=0..K exp(zk) Hidden layer: vector h1 (NB: This is just logistic regression) Input layer: vector x CS447: Natural Language Processing (J. Hockenmaier) !5 CS447: Natural Language Processing (J. Hockenmaier) !6 Neural Language Models LMs define a distribution over strings: P(w1….wk) LMs factor P(w1….wk) into the probability of each word: " P(w1….wk) = P(w1)·P(w2|w1)·P(w3|w1w2)·…· P(wk | w1….wk#1) A neural LM needs to define a distribution over the V words in Neural Language the vocabulary, conditioned on the preceding words. -
Synthesis and Recognition of Speech Creating and Listening to Speech
ISSN 1883-1974 (Print) ISSN 1884-0787 (Online) National Institute of Informatics News NII Interview 51 A Combination of Speech Synthesis and Speech Oct. 2014 Recognition Creates an Affluent Society NII Special 1 “Statistical Speech Synthesis” Technology with a Rapidly Growing Application Area NII Special 2 Finding Practical Application for Speech Recognition Feature Synthesis and Recognition of Speech Creating and Listening to Speech A digital book version of “NII Today” is now available. http://www.nii.ac.jp/about/publication/today/ This English language edition NII Today corresponds to No. 65 of the Japanese edition [Advance Notice] Great news! NII Interview Yamagishi-sensei will create my voice! Yamagishi One result is a speech translation sys- sound. Bit (NII Character) A Combination of Speech Synthesis tem. This system recognizes speech and translates it Ohkawara I would like your comments on the fu- using machine translation to synthesize speech, also ture challenges. and Speech Recognition automatically translating it into every language to Ono The challenge for speech recognition is how speak. Moreover, the speech is created with a human close it will come to humans in the distant speech A Word from the Interviewer voice. In second language learning, you can under- case. If this study is advanced, it will be possible to Creates an Affluent Society stand how you should pronounce it with your own summarize the contents of a meeting and to automati- voice. If this is further advanced, the system could cally take the minutes. If a robot understands the con- have an actor in a movie speak in a different language tents of conversations by multiple people in a natural More and more people have begun to use smart- a smartphone, I use speech input more often. -
Natural Language Processing in Speech Understanding Systems
Working Paper Series ISSN 11 70-487X Natural language processing in speech understanding systems by Dr Geoffrey Holmes Working Paper 92/6 September, 1992 © 1992 by Dr Geoffrey Holmes Department of Computer Science The University of Waikato Private Bag 3105 Hamilton, New Zealand Natural Language P rocessing In Speech Understanding Systems G. Holmes Department of Computer Science, University of Waikato, New Zealand Overview Speech understanding systems (SUS's) came of age in late 1071 as a result of a five year devel opment. programme instigated by the Information Processing Technology Office of the Advanced Research Projects Agency (ARPA) of the Department of Defense in the United States. The aim of the progranune was to research and tlevelop practical man-machine conuuunication system s. It has been argued since, t hat. t he main contribution of this project was not in the development of speech science, but in the development of artificial intelligence. That debate is beyond the scope of th.is paper, though no one would question the fact. that the field to benefit most within artificial intelligence as a result of this progranune is natural language understan ding. More recent projects of a similar nature, such as projects in the Unite<l Kiug<lom's ALVEY programme and Ew·ope's ESPRIT programme have added further developments to this important field. Th.is paper presents a review of some of the natural language processing techniques used within speech understanding syst:ems. In particular. t.ecl.miq11es for handling syntactic, semantic and pragmatic informat.ion are ,Uscussed. They are integrated into SUS's as knowledge sources. -
Arxiv:2007.00183V2 [Eess.AS] 24 Nov 2020
WHOLE-WORD SEGMENTAL SPEECH RECOGNITION WITH ACOUSTIC WORD EMBEDDINGS Bowen Shi, Shane Settle, Karen Livescu TTI-Chicago, USA fbshi,settle.shane,[email protected] ABSTRACT Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length seg- ments of frames. We consider segmental models for whole- word (“acoustic-to-word”) speech recognition, with the feature vectors defined using vector embeddings of segments. Such models are computationally challenging as the number of paths is proportional to the vocabulary size, which can be orders of magnitude larger than when using subword units like phones. We describe an efficient approach for end-to-end whole-word segmental models, with forward-backward and Viterbi de- coding performed on a GPU and a simple segment scoring function that reduces space complexity. In addition, we inves- tigate the use of pre-training via jointly trained acoustic word embeddings (AWEs) and acoustically grounded word embed- dings (AGWEs) of written word labels. We find that word error rate can be reduced by a large margin by pre-training the acoustic segment representation with AWEs, and additional Fig. 1. Whole-word segmental model for speech recognition. (smaller) gains can be obtained by pre-training the word pre- Note: boundary frames are not shared. diction layer with AGWEs. Our final models improve over segmental models, where the sequence probability is com- prior A2W models. puted based on segment scores instead of frame probabilities. Index Terms— speech recognition, segmental model, Segmental models have a long history in speech recognition acoustic-to-word, acoustic word embeddings, pre-training research, but they have been used primarily for phonetic recog- nition or as phone-level acoustic models [11–18]. -
Improving Named Entity Recognition Using Deep Learning with Human in the Loop
Demonstration Improving Named Entity Recognition using Deep Learning with Human in the Loop Ticiana L. Coelho da Silva Regis Pires Magalhães José Antônio F. de Macêdo Insight Data Science Lab Insight Data Science Lab Insight Data Science Lab Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil [email protected] [email protected] [email protected] David Araújo Abreu Natanael da Silva Araújo Vinicius Teixeira de Melo Insight Data Science Lab Insight Data Science Lab Insight Data Science Lab Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil [email protected] [email protected] [email protected] Pedro Olímpio Pinheiro Paulo A. L. Rego Aloisio Vieira Lira Neto Insight Data Science Lab Federal University of Ceara Brazilian Federal Highway Police Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil Fortaleza, Ceara, Brazil [email protected] [email protected] [email protected] ABSTRACT Even, orthographic features and language-specific knowledge Named Entity Recognition (NER) is a challenging problem in Nat- resources as gazetteers are widely used in NER, such approaches ural Language Processing (NLP). Deep Learning techniques have are costly to develop, especially for new languages and new do- been extensively applied in NER tasks because they require little mains, making NER a challenge to adapt to these scenarios[10]. feature engineering and are free from language-specific resources, Deep Learning models have been extensively used in NER learning important features from word or character embeddings tasks [3, 5, 9] because they require little feature engineering and trained on large amounts of data. However, these techniques they may learn important features from word or character embed- are data-hungry and require a massive amount of training data. -
Discriminative Transfer Learning for Optimizing ASR and Semantic Labeling in Task-Oriented Spoken Dialog
Discriminative Transfer Learning for Optimizing ASR and Semantic Labeling in Task-oriented Spoken Dialog Yao Qian, Yu Shi and Michael Zeng Microsoft Speech and Dialogue Research Group, Redmond, WA, USA fyaoqian,yushi,[email protected] Abstract universal representation for the common-sense knowledge and lexical semantics hiding in the data. Adapting pre-trained trans- Spoken language understanding (SLU) tries to decode an in- former to ASR lattice was proposed to SDS in [7], where lattice put speech utterance such that effective semantic actions can reachability masks and lattice positional encoding were utilized be taken to continue meaningful and interactive spoken dialog to enable lattice inputs during fine-tuning. In [8], ASR-robust (SD). The performance of SLU, however, can be adversely af- contextualized embeddings were learned by using word acous- fected by automatic speech recognition (ASR) errors. In this tic confusion. The studies on ATIS corpus [9] have shown that paper, we exploit transfer learning in a Generative pre-trained both approaches could improve the robustness of NLU to ASR Transformer (GPT) to jointly optimize ASR error correction errors. and semantic labeling in terms of dialog act and slot-value for a given user’s spoken response in the context of SD system Meanwhile, many researchers tried to skip ASR entirely or (SDS). With the encoded ASR output and dialog history as use only partial information, e.g., phone sequence instead of context, a conditional generative model is trained to generate word sequence, extracted from its modules for SLU [10–12]. transcripts correction, dialog act, and slot-values successively. In [10], it has studied techniques for building call routers from The proposed generation model is jointly optimized as a clas- scratch without any knowledge of the application vocabulary sification task, which utilizes the ground-truth and N-best hy- or grammar. -
Overview of Lstms and Word2vec and a Bit About Compositional Distributional Semantics If There’S Time
Overview of LSTMs and word2vec and a bit about compositional distributional semantics if there’s time Ann Copestake Computer Laboratory University of Cambridge November 2016 Outline RNNs and LSTMs Word2vec Compositional distributional semantics Some slides adapted from Aurelie Herbelot. Outline. RNNs and LSTMs Word2vec Compositional distributional semantics Motivation I Standard NNs cannot handle sequence information well. I Can pass them sequences encoded as vectors, but input vectors are fixed length. I Models are needed which are sensitive to sequence input and can output sequences. I RNN: Recurrent neural network. I Long short term memory (LSTM): development of RNN, more effective for most language applications. I More info: http://neuralnetworksanddeeplearning.com/ (mostly about simpler models and CNNs) https://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ Sequences I Video frame categorization: strict time sequence, one output per input. I Real-time speech recognition: strict time sequence. I Neural MT: target not one-to-one with source, order differences. I Many language tasks: best to operate left-to-right and right-to-left (e.g., bi-LSTM). I attention: model ‘concentrates’ on part of input relevant at a particular point. Caption generation: treat image data as ordered, align parts of image with parts of caption. Recurrent Neural Networks http://colah.github.io/posts/ 2015-08-Understanding-LSTMs/ RNN language model: Mikolov et al, 2010 RNN as a language model I Input vector: vector for word at t concatenated to vector which is output from context layer at t − 1. I Performance better than n-grams but won’t capture ‘long-term’ dependencies: She shook her head. -
Feedforward Neural Networks and Word Embeddings
Feedforward Neural Networks and Word Embeddings Fabienne Braune1 1LMU Munich May 14th, 2017 Fabienne Braune (CIS) Feedforward Neural Networks and Word Embeddings May 14th, 2017 · 1 Outline 1 Linear models 2 Limitations of linear models 3 Neural networks 4 A neural language model 5 Word embeddings Fabienne Braune (CIS) Feedforward Neural Networks and Word Embeddings May 14th, 2017 · 2 Linear Models Fabienne Braune (CIS) Feedforward Neural Networks and Word Embeddings May 14th, 2017 · 3 Binary Classification with Linear Models Example: the seminar at < time > 4 pm will Classification task: Do we have an < time > tag in the current position? Word Lemma LexCat Case SemCat Tag the the Art low seminar seminar Noun low at at Prep low stime 4 4 Digit low pm pm Other low timeid will will Verb low Fabienne Braune (CIS) Feedforward Neural Networks and Word Embeddings May 14th, 2017 · 4 Feature Vector Encode context into feature vector: 1 bias term 1 2 -3 lemma the 1 3 -3 lemma giraffe 0 ... ... ... 102 -2 lemma seminar 1 103 -2 lemma giraffe 0 ... ... ... 202 -1 lemma at 1 203 -1 lemma giraffe 0 ... ... ... 302 +1 lemma 4 1 303 +1 lemma giraffe 0 ... ... ... Fabienne Braune (CIS) Feedforward Neural Networks and Word Embeddings May 14th, 2017 · 5 Dot product with (initial) weight vector 2 3 2 3 x0 = 1 w0 = 1:00 6 x = 1 7 6 w = 0:01 7 6 1 7 6 1 7 6 x = 0 7 6 w = 0:01 7 6 2 7 6 2 7 6 ··· 7 6 ··· 7 6 7 6 7 6x = 17 6x = 0:017 6 101 7 6 101 7 6 7 6 7 6x102 = 07 6x102 = 0:017 T 6 7 6 7 h(X )= X · Θ X = 6 ··· 7 Θ = 6 ··· 7 6 7 6 7 6x201 = 17 6x201 = 0:017 6 7