Speech recognition based on phonetic features and acoustic landmarks

A probabilistic and statistical framework is presented for automatic speech recognition based on a phonetic feature representation of speech sounds. In this acoustic-phonetic approach, the speech recognition problem is hypothesized as a maximization of the joint posterior probability of a set of phonetic features and the corresponding acoustic landmarks. Binary classifiers of the manner phonetic features - syllabic, sonorant and continuant - are applied for the probabilistic detection of speech landmarks. The landmarks include stop bursts, vowel onsets, syllabic peaks, syllabic dips, fricative onsets and offsets, and sonorant consonant onsets and offsets. The classifiers use automatically extracted knowledge based acoustic parameters (APs) that are acoustic correlates of those phonetic features. For isolated word recognition with known and limited vocabulary, the landmark sequences are constrained using a manner class pronunciation graph. Probabilistic decisions on place and voicing phonetic features are then made using a separate set of APs extracted using the landmarks. The framework exploits two properties of the knowledge-based acoustic cues of phonetic features: (1) sufficiency of the acoustic cues of a phonetic feature for a decision on that feature and (2) invariance of the acoustic cues with respect to context. The probabilistic framework makes the acoustic-phonetic approach to speech recognition suitable for practical recognition tasks as well as compatible with probabilistic pronunciation and language models. Support vector machines (SVMs) are applied for the binary classification tasks because of their two favorable properties - good generalization and the ability to learn from a relatively small amount of high dimensional data. Performance comparable to Hidden Markov Model (HMM) based systems is obtained on landmark detection as well as isolated word recognition. Applications to rescoring of lattices from a large vocabulary continuous speech recognizer are also presented

Controlling intense, ultrashort, laser-driven relativistic mega-ampere electron fluxes by a modest, static magnetic field

The guiding and control of ultrahigh flux, femtosecond relativistic electron pulses through solid density matter is of great importance for many areas of high energy density science. Efforts so far include the use of magnetic fields generated by the propagation of the electron pulse itself or the application of hundreds of Tesla magnitudes, pulsed external magnetic fields driven by either short pulse lasers or electrical pulses. Here we experimentally demonstrate the guiding of hundreds of keV mega-ampere electron pulses in a magnetized neodymium solid that has a very modest, easily available static field of 0.1 tesla. The electron pulses driven by an ultrahigh intensity, 30 femtosecond laser are shown to propagate beam-like, a distance as large as 5 mm in a high Z target (neodymium), their collimation improved and flux density enhanced nearly by a factor of 3. Particle-in-cell simulations in the appropriate parameter regime match the experimental observations. In addition, the simulations predict the occurrence of a novel, near-monochromatic feature towards the high energy end of the electron energy spectrum, which is tunable by the applied magnetic field strength. These results may prove valuable for fast electron beam-driven radiation sources, fast ignition of laser fusion, and laboratory astrophysics.Comment: 10 pages, 5 figure

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients

Juneja et al. Significance of Invariant Acoustic Cues From Sound to SIGNIFICANCE OF INVARIANT ACOUSTIC CUES IN A PROBABILISTIC FRAMEWORK FOR LANDMARK-BASED SPEECH RECOGNITION

ABSTRACT A probabilistic framework for landmark-based speech recognition that utilizes the sufficiency and context invariance properties of acoustic cues for phonetic features is presented. Binary classifiers of the manner phonetic features "sonorant", "continuant" and "syllabic" operate on each frame of speech, each using a small number of relevant and sufficient acoustic parameters to generate probabilistic landmark sequences. The relative nature of the parameters developed for the extraction of acoustic cues for manner phonetic features makes them "invariant" of the manner of neighboring speech frames. This invariance of manner acoustic cues makes the use of only those three classifiers along with the speech/silence classifier complete irrespective of the manner context. The obtained landmarks are then used to extract relevant acoustic cues to make probabilistic binary decisions for the place and voicing phonetic features. Similar to the invariance property of the manner acoustic cues, the acoustic cues for place phonetic features extracted using manner landmarks are invariant of the place of neighboring sounds. Pronunciation models based on phonetic features are used to constrain the landmark sequences and to narrow the classification of place and voicing. Preliminary results have been obtained for manner recognition and the corresponding landmarks. Using classifiers trained from the phonetically rich TIMIT database, 80.2% accuracy was obtained for broad class recognition of the isolated digits in the TIDIGITS database which compares well with the accuracies of 74.8% and 81.0% obtained by a hidden Markov model (HMM) based system using mel-frequency cepstral coefficients (MFCCs) and knowledge-based parameters, respectively. INTRODUCTION A probabilistic framework for a landmark-based approach to speech recognition based on representation of speech sounds by bundles of binary-valued phonetic feature