Revisiting a Classic Problem in Statutory Interpretation: Is a Minister a Laborer?

This study presents a new analysis of an iconic United States Supreme Court case, Holy Trinity Church v. United States (1892). The question in Holy Trinity Church concerned whether a law making it illegal to pay the transportation of a person entering the U.S. under contract to perform “labor or service of any kind” applied to a wealthy Manhattan church that had paid to bring its new rector from England to New York. The Supreme Court unanimously ruled that the law did not apply to the church’s contract, relying first on the ordinary meaning of “labor” and second on the legislative history of the single construction “labor or service.” Highlighting the use of corpus linguistic methods, this study tests the arguments presented by the Court and reveals new insights through an analysis of historic and contemporary reference corpora and a specialized corpus of U.S. statutes. The results demonstrate that the disjunctive phrase “labor or service” appeared to be a legal term of art with narrow interpretation that would exclude clergy, but around the time of Holy Trinity Church, slight variations on the phrase (e.g., pluralization, conjunction, and modification) applied to contexts with broader meaning. When examining “labor” as an independent term, those who labored were generally not clergy and the description of the activities of clergy was typically not described as labor, although examination evidenced instances of both. The findings demonstrate the importance of consulting corpora in the evaluation of statutory and ordinary meaning and considering the sociohistorical contexts in which it occurs

I-vector estimation using informative priors for adaptation of deep neural networks

This is the author accepted manuscript. The final version is available from ISCA via http://www.isca-speech.org/archive/interspeech_2015/i15_2872.html Supporting data for this paper is available at the http://www.repository.cam.ac.uk/handle/1810/248387 data repository.I-vectors are a well-known low-dimensional representation of speaker space and are becoming increasingly popular in adaptation of state-of-the-art deep neural network (DNN) acoustic models. One advantage of i-vectors is that they can be used with very little data, for example a single utterance. However, to improve robustness of the i-vector estimates with limited data, a prior is often used. Traditionally, a standard normal prior is applied to i-vectors, which is nevertheless not well suited to the increased variability of short utterances. This paper proposes a more informative prior, derived from the training data. As well as aiming to reduce the non-Gaussian behaviour of the i-vector space, it allows prior information at different levels, for example gender, to be used. Experiments on a US English Broadcast News (BN) transcription task for speaker and utterance i-vector adaptation show that more informative priors reduce the sensitivity to the quantity of data used to estimate the i-vector. The best configuration for this task was utterance-level test i-vectors enhanced with informative priors which gave a 13% relative reduction in word error rate over the baseline (no i-vectors) and a 5% over utterance-level test i-vectors with standard prior.This work was supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology)

Waveform-based speaker representations for speech synthesis

Speaker adaptation is a key aspect of building a range of speech processing systems, for example personalised speech synthesis. For deep-learning based approaches, the model parameters are hard to interpret, making speaker adaptation more challenging. One widely used method to address this problem is to extract a fixed length vector as speaker representation, and use this as an additional input to the task-specific model. This allows speaker-specific output to be generated, without modifying the model parameters. However, the speaker representation is often extracted in a task-independent fashion. This allows the same approach to be used for a range of tasks, but the extracted representation is unlikely to be optimal for the specific task of interest. Furthermore, the features from which the speaker representation is extracted are usually pre-defined, often a standard speech representation. This may limit the available information that can be used. In this paper, an integrated optimisation framework for building a task specific speaker representation, making use of all the available information, is proposed. Speech synthesis is used as the example task. The speaker representation is derived from raw waveform, incorporating text information via an attention mechanism. This paper evaluates and compares this framework with standard task-independent forms.EPSRC International Doctoral Scholarship, reference number 10348827; St. John’s College Internal Graduate Scholarship; the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 655764; EPSRC grant EP/I031022/1 (Natural Speech Technology

Trapping and relocating seals from salmonid fish farms in Tasmania, 1990-2000: was it a success?

In an effort to reduce the impact of seals on fish farms, the trapping and relocation of seals at Tasmanian salmonid farms began in 1990. To the end of May 2000,353 identified individual seals had been trapped in 672 capture events. Most were non-breeding male Australian fur seals (Arctocephalus pusillus doriftrus). The number of seals captured increased (from four in 1990 ro a peak of 164 in 1998) with the size and extent of the farms, and an increase in salmon production from 55 tonnes in 1986/87 to almost 10000 tonnes in 1999/2000. Of 586 capture events 52% were of seals that had been captured more than once. When seals are recaptured following trapping and relocation, this occurs on average 25 days after capture. Capture-mark-recapture calculations show that many seals in the vicinity of fish farms are not 'trappable', suggesting that trapping is only effective for certain individuals. Some individuals are recaptured many times, reflecting the predisposition of some individuals to be captured ('trap-happy'). Interaction is seasonal, with most seals trapped during winter, between May and September. The assessment of trends in capture rates is problematic, due to the lack of capture effort information from the farms. A further confounding factor has been the change in management practice both between farms and over time, as the use of predator nets has become more widespread. Two seals trapped at fish farms and fitted with satellite transmitters before relocation have either not returned to the farm or returned to the vicinity of farms and not interacted with them, although on one occasion the individual was trapped. The effectiveness of the relocation program as a management tool to reduce seal interactions cannot be quantified from the relocation data per se, but relocation does not stop seals interacting with farms

1-Hydr­oxy-3-(3-methyl­but-2-en­yloxy)xanthone

In the title compound, C18H16O4, a monoprenylated xanthone, the xanthone skeleton exhibits an essentially planar conformation (r.m.s. deviation 0.0072 Å) and the isoprenyl side chain remains approximately in the mean plane of the xanthone unit, making a dihedral angle of 4.5 (2)°. The hydroxyl group forms an intra­molecular O—H⋯O hydrogen bond. Moreover, there is a weak inter­molecular C—H⋯O inter­action between a ring C atom and the xanthene O atom. In the crystal structure, there are no inter­molecular hydrogen bonds and the crystallographic packing is governed by van der Waals forces, leading to an arrangement in which the mol­ecules assemble with their planes parallel to each other, having a separation of 3.6 (3) Å

Adapting an Unadaptable ASR System

As speech recognition model sizes and training data requirements grow, it is increasingly common for systems to only be available via APIs from online service providers rather than having direct access to models themselves. In this scenario it is challenging to adapt systems to a specific target domain. To address this problem we consider the recently released OpenAI Whisper ASR as an example of a large-scale ASR system to assess adaptation methods. An error correction based approach is adopted, as this does not require access to the model, but can be trained from either 1-best or N-best outputs that are normally available via the ASR API. LibriSpeech is used as the primary target domain for adaptation. The generalization ability of the system in two distinct dimensions are then evaluated. First, whether the form of correction model is portable to other speech recognition domains, and secondly whether it can be used for ASR models having a different architecture.Comment: submitted to INTERSPEEC

Adapting an ASR Foundation Model for Spoken Language Assessment

A crucial part of an accurate and reliable spoken language assessment system is the underlying ASR model. Recently, large-scale pre-trained ASR foundation models such as Whisper have been made available. As the output of these models is designed to be human readable, punctuation is added, numbers are presented in Arabic numeric form and abbreviations are included. Additionally, these models have a tendency to skip disfluencies and hesitations in the output. Though useful for readability, these attributes are not helpful for assessing the ability of a candidate and providing feedback. Here a precise transcription of what a candidate said is needed. In this paper, we give a detailed analysis of Whisper outputs and propose two solutions: fine-tuning and soft prompt tuning. Experiments are conducted on both public speech corpora and an English learner dataset. Results show that we can effectively alter the decoding behaviour of Whisper to generate the exact words spoken in the response.Comment: Proceedings of SLaT