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--Combined Company to Launch Slate of High-Profile Films for Theatrical, Home Video and International Release-- LOS ANGELES, April 26, 2017 /PRNewswire/ -- Feature Film Production Company Crimson Forest Entertainment Group Inc. (OTC: CRIM) and specialty theatrical and home video distributor Hannover House, Inc. (OTC: HHSE) have confirmed plans to merge operations under a stock-swap and financing plan, scheduled to be effective as of May 1, 2017. The combined company will immediately launch production activities on a slate of high-profile feature films that will drive the theatrical, home video and international release schedules for the coming years. Formed in 2010, and financed with investment capital and presales from China, Crimson Forest Entertainment has successfully financed and acquired several films for international and North America distribution. PALI ROAD was the first Hawaii-China co-production, starring Jackson Rathbone from the "The Twilight Saga," Sung Kang from "Fast & Furious," Henry Ian Cusick, known for his roles in "L.O.S.T.,"  and "The 100," and Chinese Celebrity Michelle Chen. The film premiered at the Hawaii International Film Festival and went on to win several awards, including "Best Actress" "Best Cinematography" and a "Best Director" award at the 12th Annual Chinese American Film Festival. The film was released theatrically, both in North America and China, and in Malaysia earlier this month. Formed in 1993 and growing into one of the top independent distributed labels in North America, Hannover House, Inc. has direct distribution relationships for all major theatre circuits, principal media outlets, and wholesale access to major home video retailers and mass merchants.  Hannover House has released more than 50 films to theatres and more than 300 titles to the Home Video Market in the United States, including titles such as "Grand Champion" (with Bruce Willis, Julia Roberts and George Strait) and director Joel Schumacher's teen angst thriller "Twelve" (starring Curtis "50-Cent" Jackson, Emma Roberts, Ellen Barkin and Chase Crawford). "There is a growing need for specialty independent distributors," said Jonathan Lim, CEO of Crimson Forest Entertainment. "There is a lot of quality product out there that is being ignored and we are excited that Hannover House has partnered with us in releasing these films. It will bring much needed diversity to audiences in North America, and growing commercial success for the combined company," he concluded. "A Crimson Forest and Hannover House merger is expected to fill the demand from independent and international productions, which seek distributors that have direct access to theatrical, as well as Home Video and VOD & Digital sales," said Eric Parkinson, CEO of Hannover House. "We're optimistic about what these new opportunities and corporate structure will bring to Hannover House and our shareholders and excited by the upcoming titles that we will be announcing and releasing in the upcoming weeks, which we fully anticipate will have a substantial impact on the growth of our combined company, " said Parkinson. One of the first new titles to be released under the combined Crimson Forest -- Hannover House structure is the $20-million dollar action thriller feature "Shockwave" starring Andy Lau and Jiang Wu. The film will be co-released together with CMC Pictures in North America next month. Under the newly merged company, the board of directors will be comprised as follows: Jonathan Lim (Chairman), Eric Parkinson (CEO), Fred Shefte (President) and Tom Sims (V.P. Sales).  The existing offices for Hannover House, Inc. and its affiliate Medallion Releasing, Inc. in Fayetteville, Arkansas will remain as the primary distribution operations office.  The Los Angeles offices for Crimson Forest will serve as the company's corporate and production headquarters, and the Crimson Forest office in Shanghai, China, will continue to operate as the finance office for the funding of new productions and releasing costs. Crimson Forest is also negotiating for the acquisition of other, complementary media companies to add to the enhanced distribution entity. Included in the corporate merger are Hannover House affiliates, Medallion Releasing, Inc. and Bookworks, Inc., respectively handling theatrical and publishing ventures. For more information, contact ERIC PARKINSON, Hannover House, Inc. / Medallion Releasing, Inc., 479-521-5774 or 818-481-5277, Eric@HannoverHouse.com.


Avellaneda F.,CRIM | Dal Zilio S.,CNRS Laboratory for Analysis and Architecture of Systems | Raclet J.-B.,French National Center for Scientific Research
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

We define a new subclass of nondeterministic finite automata for prefix-closed languages called Flanked Finite Automata (FFA). Our motivation is to provide an efficient way to compute the quotient and inclusion of regular languages without the need to determinize the underlying automata. These operations are the building blocks of several verification algorithms that can be interpreted as language equation solving problems. We provide a construction for computing a FFA accepting the quotient and product of languages that is compositional and that does not incur an exponential blow up in size. This makes flanked automata a good candidate as a formalism for compositional design and verification of systems. © Springer International Publishing AG 2016.


Djado K.,CRIM | Egli R.,Université de Sherbrooke | Granger F.,Cyanide France
Computer Animation and Virtual Worlds | Year: 2012

This paper presents a method for simulating the motion of water drops on a surface in real time. We describe the dynamics of a drop moving on the surface, and then we present our simulation model. We use a geometry-based representation of a drop. Each drop is modeled by a deformable 3D mesh. This geometrical representation allows drops to be on the surface or in the air. We also propose a simple method to handle drop merging and separation. For the rendering, we simulate reflection and refraction. The drop trace is also taken into account. Our method is fast and robust and yields realistic results when applied to treat condensation on a surface or human sweating in real time. Copyright © 2012 John Wiley & Sons, Ltd.


Alam M.J.,University of Quebec at Montréal | Kenny P.,CRIM | O'Shaughnessy D.,University of Quebec at Montréal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2013

This paper presents regularized minimum variance distortion-less response (MVDR)-based cepstral features for robust continuous speech recognition. The mel-frequency cepstral coefficient (MFCC) features, widely used in speech recognition tasks, are usually computed from a direct spectrum estimate, that is, the squared magnitude of the discrete Fourier transform (DFT) of speech frames. Direct spectrum estimation methods (also known as nonparametric estimators) perform poorly under noisy and adverse conditions. To reduce this performance drop we propose to increase robustness of the speech recognition system by extracting more robust features based on the regularized MVDR technique. The proposed method, when evaluated on the AURORA-4 speech recognition task, provides an average relative improvement in word accuracy of 11.3%, 6.1%, and 5.2% over the conventional MFCC, PLP, MVDR and PMVDR-based MFCC features, respectively. © 2013 IEEE.


Alam M.J.,University of Quebec at Montréal | Kenny P.,CRIM | O'Shaughnessy D.,University of Quebec at Montréal
Digital Signal Processing: A Review Journal | Year: 2014

In this paper we introduce a robust feature extractor, dubbed as robust compressive gammachirp filterbank cepstral coefficients (RCGCC), based on an asymmetric and level-dependent compressive gammachirp filterbank and a sigmoid shape weighting rule for the enhancement of speech spectra in the auditory domain. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. As a post processing scheme we employ a short-time feature normalization technique called short-time cepstral mean and scale normalization (STCMSN), which, by adjusting the scale and mean of cepstral features, reduces the difference of cepstra between the training and test environments. For performance evaluation, in the context of speech recognition, of the proposed feature extractor we use the standard noisy AURORA-2 connected digit corpus, the meeting recorder digits (MRDs) subset of the AURORA-5 corpus, and the AURORA-4 LVCSR corpus, which represent additive noise, reverberant acoustic conditions and additive noise as well as different microphone channel conditions, respectively. The ETSI advanced front-end (ETSI-AFE), the recently proposed power normalized cepstral coefficients (PNCC), conventional MFCC and PLP features are used for comparison purposes. Experimental speech recognition results demonstrate that the proposed method is robust against both additive and reverberant environments. The proposed method provides comparable results to that of the ETSI-AFE and PNCC on the AURORA-2 as well as AURORA-4 corpora and provides considerable improvements with respect to the other feature extractors on the AURORA-5 corpus. © 2014 Elsevier Inc.


Alam M.J.,University of Quebec at Montréal | Kenny P.,CRIM | O'Shaughnessy D.,University of Quebec at Montréal
Cognitive Computation | Year: 2013

In this paper, we investigate low-variance multitaper spectrum estimation methods to compute the mel-frequency cepstral coefficient (MFCC) features for robust speech and speaker recognition systems. In speech and speaker recognition, MFCC features are usually computed from a single-tapered (e.g., Hamming window) direct spectrum estimate, that is, the squared magnitude of the Fourier transform of the observed signal. Compared with the periodogram, a power spectrum estimate that uses a smooth window function, such as Hamming window, can reduce spectral leakage. Windowing may help to reduce spectral bias, but variance often remains high. A multitaper spectrum estimation method that uses well-selected tapers can gain from the bias-variance trade-off, giving an estimate that has small bias compared with a single-taper spectrum estimate but substantially lower variance. Speech recognition and speaker verification experimental results on the AURORA-2 and AURORA-4 corpora and the NIST 2010 speaker recognition evaluation corpus (telephone as well as microphone speech), respectively, show that the multitaper methods perform better compared with the Hamming-windowed spectrum estimation method. In a speaker verification task, compared with the Hamming window technique, the sinusoidal weighted cepstrum estimator, multi-peak, and Thomson multitaper techniques provide a relative improvement of 20.25, 18.73, and 12.83 %, respectively, in equal error rate. © 2012 Springer Science+Business Media New York.


Alam M.J.,University of Quebec at Montréal | O'Shaughnessy D.,University of Quebec at Montréal | Kenny P.,CRIM
2013 8th International Workshop on Systems, Signal Processing and Their Applications, WoSSPA 2013 | Year: 2013

This paper presents a novel feature extractor for robust large vocabulary continuous speech recognition (LVCSR) task. For accurate and robust estimation of speech power spectrum we propose to compute the features from the regularized minimum variance distortionless response (regMVDR) spectral estimate instead of the windowed periodogram estimate. A sigmoid shape subband spectrum enhancement technique and a short-time feature normalization, known as short-time mean and scale normalization (STMSN), are also used for robust estimation of the cepstral features for speech recognition task. When evaluated on the AURORA-4 LVCSR corpus proposed feature extractor provides an average relative improvement of 38.5%,35.0%, and 34.3%,30.7%,5.6%, and 7.1% over the MFCC, PLP, MVDR-based MFCC, regMVDR-based MFCC, PNCC and the robust feature extractor of [4], respectively, in terms of the recognition accuracy. © 2013 IEEE.


Alam M.J.,University of Québec | Kinnunen T.,University of Eastern Finland | Kenny P.,CRIM | Ouellet P.,CRIM | O'Shaughnessy D.,University of Québec
Speech Communication | Year: 2013

In this paper we study the performance of the low-variance multi-taper Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features in a state-of-the-art i-vector speaker verification system. The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate. Such a single-tapered spectrum estimate has large variance, which can be reduced by averaging spectral estimates obtained using a set of different tapers, leading to a so-called multi-taper spectral estimate. The multi-taper spectrum estimation method has proven to be powerful especially when the spectrum of interest has a large dynamic range or varies rapidly. Multi-taper MFCC features were also recently studied in speaker verification with promising preliminary results. In this study our primary goal is to validate those findings using an up-to-date i-vector classifier on the latest NIST 2010 SRE data. In addition, we also propose to compute robust perceptual linear prediction (PLP) features using multitapers. Furthermore, we provide a detailed comparison between different taper weight selections in the Thomson multi-taper method in the context of speaker verification. Speaker verification results on the telephone (det5) and microphone speech (det1, det2, det3 and det4) of the latest NIST 2010 SRE corpus indicate that the multi-taper methods outperform the conventional periodogram technique. Instead of simply averaging (using uniform weights) the individual spectral estimates in forming the multi-taper estimate, weighted averaging (using non-uniform weights) improves performance. Compared to the MFCC and PLP baseline systems, the sine-weighted cepstrum estimator (SWCE) based multitaper method provides average relative reductions of 12.3% and 7.5% in equal error rate, respectively. For the multi-peak multi-taper method, the corresponding reductions are 12.6% and 11.6%, respectively. Finally, the Thomson multi-taper method provides error reductions of 9.5% and 5.0% in EER for MFCC and PLP features, respectively. We conclude that both the MFCC and PLP features computed via multitapers provide systematic improvements in recognition accuracy. © 2012 Elsevier B.V. All rights reserved.


Alam M.J.,CRIM | Kinnunen T.,University of Eastern Finland | Kenny P.,CRIM | Ouellet P.,CRIM | O'Shaughnessy D.,INRS EMT
2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings | Year: 2011

This paper studies the low-variance multi-taper mel-frequency cepstral coefficient (MFCC) features in the state-of-the-art speaker verification. The MFCC features are usually computed using a Hamming-windowed DFT spectrum. Windowing reduces the bias of the spectrum but variance remains high. Recently, low-variance multi-taper MFCC features were studied in speaker verification with promising preliminary results on the NIST 2002 SRE data using a simple GMM-UBM recognizer. In this study our goal is to validate those findings using a up-to-date i-vector classifier on the latest NIST 2010 SRE data. Our experiment on the telephone (det5) and microphone speech (det1, det2, det3 and det4) indicate that the multi-taper approaches perform better than the conventional Hamming window technique. © 2011 IEEE.


Patent
Crim | Date: 2010-01-13

Occurrences of one or more keywords in audio data are identified using a speech recognizer employing a language model to derive a transcript of the keywords. The transcript is converted into a phoneme sequence. The phonemes of the phoneme sequence are mapped to the audio data to derive a time-aligned phoneme sequence that is searched for occurrences of keyword phoneme sequences corresponding to the phonemes of the keywords. Searching includes computing a confusion matrix. The language model used by the speech recognizer is adapted to keywords by increasing the likelihoods of the keywords in the language model. For each potential occurrences keywords detected, a corresponding subset of the audio data may be played back to an operator to confirm whether the potential occurrences correspond to actual occurrences of the keywords.

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